JP6902057B2 - Industrial equipment monitoring equipment - Google Patents

Description

The present invention relates to an industrial equipment monitoring device for monitoring industrial equipment such as an air compressor and a monitoring method for the industrial equipment.

Since air conditioners suddenly start to be used in each home as the temperature changes, problems such as the number of requests for repairs will increase as failures that were not found during the period of non-use are discovered for the first time at the start of use. is there. To solve this problem, while acquiring driving information from the equipment, the driving information for a preset period is recorded as past driving information, and the current driving information and the past driving information are compared, and the comparison result is obtained. There is a conventional technique for predicting the failure stop time based on the above and displaying the failure stop time. Patent Document 1 describes an example of the above-mentioned prior art.

Japanese Unexamined Patent Publication No. 2013-213669

Industrial equipment is widely used as production equipment in factories, social infrastructure equipment, ancillary equipment in buildings, transportation equipment, and the like. Such industrial equipment needs to maintain safety, operating rate, and equipment efficiency, and is managed in various ways.

There is an air compressor as one of the industrial equipment. Air compressors are used in various industrial fields, and at production sites, they provide compressed air to end equipment such as air tools, air presses, and spray guns. Therefore, it can be said that the operating rate of the air compressor directly affects the production line, but if a sudden failure occurs in the air compressor, the productivity will be significantly reduced.

Such sudden failures of the air compressor are often caused by the replacement of consumable parts and the improper daily maintenance work. Consumable parts of the air compressor include, for example, a suction filter provided on the suction side of the compressor body to remove impurities in the intake air, a lubricating oil supplied to the compressor body, and a lubricating oil from the compressed air generated by the compressor body. There are separator elements that separate the components, oil filters that remove impurities in the lubricating oil, and so on.

In an air compressor, serious failure will occur unless these consumable parts are replaced at appropriate times and grease is applied to the motor bearings. For example, if the grease-up work on the motor bearing is not performed properly, the bearing will have poor lubrication, and the cage of the bearing and the outer ring and inner ring will be damaged and need to be replaced. Furthermore, if the condition is left unattended, the bearing will seize on the motor, and the motor itself will need to be replaced.

Further, the grease-up work on the motor bearing has a small work time and work cost, but if this work is neglected, downtime such as bearing replacement and motor replacement, and a failure event with a larger loss cost will occur. This is directly linked to causing a decrease in user productivity. Therefore, it is important to carry out maintenance work at an appropriate timing.

Here, an event in which replacement of consumable parts and daily maintenance work are not performed at an appropriate timing and is delayed will be referred to as "maintenance failure" hereafter.

In order to avoid this maintenance failure, the manufacturer publishes recommended values for the replacement of consumable parts and maintenance work, and the recommended values for the implementation time interval. However, the deterioration speed of consumable parts of an air compressor differs depending on the operating environment, and the air compressor does not always deteriorate as designed.

For example, there is a large difference in the deterioration speed of consumable parts between an industrial equipment manufacturing site with a large amount of dust and a food manufacturing site with a relatively small amount of dust. The above-mentioned prior art is one of the ideas for notifying the user to perform maintenance at an appropriate timing according to the actual deterioration situation in consideration of such a situation.

In the above-mentioned conventional technique, various sensors are attached to the air conditioner, and the degree of deterioration of parts is calculated based on the sensed data. Then, the next parts replacement time is predicted from the relationship between the degree of deterioration of the parts and the cumulative operating time of the air conditioner, and the user is notified. By applying this invention to industrial equipment, it is assumed that parts replacement can be promoted at an appropriate timing according to the individual deterioration rate according to the operating environment of the equipment.

However, although it is possible to promote maintenance at the optimum timing for each device by applying the conventional technology, the conventional technology takes into consideration the disadvantages of the user, which is a risk when the maintenance work is not performed. Absent.

For example, in the case of the above-mentioned poor lubrication of the motor bearing, the user is encouraged to perform the grease-up work, but the future risk such as bearing damage or motor burnout if left unattended is presented. Is not listed. In particular, since the air compressor is an industrial device that plays an important role in the industrial field as described above, a large safety margin is often taken, and even if grease-up work or proper replacement of the filter is not performed, there is a problem. I can afford to wake up for a while. In addition, even if a problem occurs, the problem is limited to an increase in power consumption and heat generation, and although it is not recommended by the manufacturer, it can often operate as an air compressor.

Therefore, for example, even if the user is notified of the grease-up work of the motor bearing, there is a risk that the user may not recognize the importance of the grease-up work from the situation where the air compressor is operating and leave it unattended. It became clear by the examination of the present inventors that there was a problem. However, in that case, the user will suffer a large loss in the future. For this reason, it has become clear from the studies by the present inventors that it is essential to present the user merits of the countermeasure work against maintenance defects in an easy-to-understand manner, especially in industrial equipment.

INDUSTRIAL APPLICABILITY According to the present invention, industrial equipment monitoring capable of detecting maintenance defects of industrial equipment such as an air compressor, notifying the user to perform maintenance, and also notifying user merits such as reduction of maintenance cost by performing maintenance. Provide a monitoring method for equipment and industrial equipment.

The present invention includes a plurality of means for solving the above problems. For example, a maintenance failure event of the industrial equipment is detected based on sensor data measured by various sensors attached to the industrial equipment. The maintenance failure detection unit, the failure risk estimation unit that estimates the failure mode that occurs over time when the maintenance failure detection unit detects the maintenance failure event, and the maintenance failure detection unit, and the maintenance The recovery time and recovery cost required to improve the failure mode based on the failure event and restore the normal state , the maintenance measures for the maintenance failure event, and the work time and work cost required for the maintenance measures are estimated and estimated. It is provided with a maintenance cost estimation unit that displays the recovery time, the recovery cost, and the maintenance failure event, and outputs the estimated work time and work cost as a display signal for comparison with the recovery time and the recovery cost. It is characterized by.

According to the present invention, it is possible to detect a maintenance defect of an industrial device, notify a user to perform maintenance, and also notify a user's merit such as reduction of maintenance cost by performing maintenance. Issues, configurations and effects other than those mentioned above will be clarified by the description of the following examples.

This is the overall configuration of the air compressor including the monitoring device of the first embodiment. This is an example of the configuration of the monitoring device of the first embodiment. This is an example showing the configuration of the diagnostic model storage unit of the monitoring device of the first embodiment. This is an example showing the configuration of the failure mode correspondence table of the monitoring device of the first embodiment. This is an example showing the processing flow of the maintenance effect estimation unit of the monitoring device of the first embodiment. This is an example showing the configuration of the recovery cost and the recovery time stored in the maintenance record data storage unit of the monitoring device of the first embodiment. This is an example showing a graph image of the failure occurrence rate stored in the maintenance record data storage unit of the monitoring device of the first embodiment. This is an example of a display screen displayed by the monitoring device of the first embodiment. This is another example of the display screen displayed by the monitoring device of the first embodiment. This is an example of the configuration of the monitoring device of the second embodiment. This is an example of a display screen displayed by the monitoring device of the second embodiment.

Hereinafter, examples of the industrial equipment monitoring device and the method for monitoring the industrial equipment of the present invention will be described with reference to the drawings.

<Example 1>
Example 1 of the industrial equipment monitoring device and the method for monitoring industrial equipment of the present invention will be described with reference to FIGS. 1 to 9.

In the following embodiment, an air compressor will be described as an example of industrial equipment. Industrial equipment is not limited to air compressors, but is equipment that operates in factories, such as motors and pumps. In particular, equipment that contains consumable parts and maintenance frequency that requires daily maintenance work are high. It is a device that has a high place.

First, the configuration of an air compressor, which is an example of the application target of the present invention, will be described with reference to FIG. FIG. 1 is a schematic view showing the configuration of an air compressor as an example.

In FIG. 1, the refueling type air compressor includes a compressor main body 1, a motor 3, an inverter 4, a suction filter 5, a suction throttle valve 6, an oil tank 7, a separator element 8, and an aftercooler 11. And have.

The compressor body 1 compresses air. The motor 3 transmits power via the belt 2 to drive the compressor main body 1. The inverter 4 variably controls the rotation speed of the motor 3. The suction filter 5 is provided on the suction side of the compressor main body 1 and removes impurities in the intake air. The suction throttle valve 6 is provided on the suction side of the compressor main body 1 to adjust the intake amount. The oil tank 7 is provided on the discharge side of the compressor main body 1 and primary separates the lubricating oil 30 from the compressed air. The separator element 8 secondarily separates the lubricating oil 30 from the compressed air separated by the oil tank 7. The aftercooler 11 cools the compressed air separated by the separator element 8 by introducing it through the pressure regulating valve 9 and the check valve 10.

In the air compressor, the lubricating oil 30 separated by the separator element 8 is supplied to the suction side of the compressor body 1. On the other hand, the lubricating oil 30 separated in the oil tank 7 is supplied to the inside of the compressor main body 1 via, for example, an oil cooler 12 for cooling the lubricating oil and an oil filter 13 for removing impurities in the lubricating oil. It has become. Further, a bypass system for bypassing the oil cooler 12 is provided, and a temperature control valve 14 for adjusting the ratio of the cooling flow rate to the oil cooler 12 side and the bypass flow rate to the bypass system is provided at the upstream connection portion of the bypass system. It is provided. The temperature control valve 14 adjusts the ratio between the cooling flow rate and the bypass flow rate according to the temperature of the lubricating oil 30 from the oil tank 7, thereby adjusting the temperature of the lubricating oil 30 supplied to the compressor main body 1. It has become. The aftercooler 11 and the oil cooler 12 are air-cooled heat exchangers, and are cooled by the cooling air generated by the cooling fan 15.

Further, a pressure sensor 208 for detecting the discharge pressure of the compressor main body 1 is provided on the downstream side of the aftercooler 11. Then, the detection signal from the pressure sensor 208 is output to the control device 17. The control device 17 calculates a deviation between the detection value of the discharge pressure input from the pressure sensor 208 and a predetermined target value set in advance, and outputs a rotation speed command signal generated based on the deviation to the inverter 4. The inverter 4 is configured to output a frequency to the motor 3 in response to a rotation speed command signal to variably control the rotation speed of the motor 3.

A vibration sensor 202 is attached to the cover portion around the bearing of the motor 3 so that poor lubrication of the bearing of the motor 3 can be detected from the measured magnitude of acceleration.

A differential pressure sensor 204 is attached to the suction filter 5, so that the state of clogging of the suction filter 5 can be detected from the differential pressure inside and outside the suction filter 5.

A differential pressure sensor 206 is attached to the separator element 8 so that the state of clogging of the separator element 8 can be detected from the differential pressure inside and outside the element.

The monitoring device 100 is connected to these various sensors 202, 204, 206, acquires measured values, deteriorates the suction filter 5 and the separator element 8 from the measured values, and lubricates the bearing of the motor 3. It is possible to grasp the status of defects.

Next, the configuration of the monitoring device 100 that monitors the air compressor as shown in FIG. 1 will be described with reference to FIG. FIG. 2 is a diagram showing an outline of the configuration of the monitoring device of this embodiment.

As shown in FIG. 2, the monitoring device (industrial equipment monitoring device) 100 includes a communication unit 102, a maintenance failure diagnosis unit (maintenance failure detection unit) 104, a diagnosis model storage unit 106, a failure risk estimation unit 108, and a failure. It is composed of a mode correspondence table 110, a maintenance effect estimation unit (maintenance cost estimation unit and a failure occurrence probability estimation unit) 112, a maintenance record data storage unit 114, an input / output control unit 116, and a display unit 118. ..

The communication unit 102 receives signals from various sensors 202, 204, 206 and various units 50, 60 outside the device. For example, the communication unit 102 is connected to a vibration sensor 202, a differential pressure sensor 204, and a differential pressure sensor 206 provided in the air compressor in order to detect the deterioration status of consumable parts and the bearing lubrication failure of the motor 3. Receive the measurement signal.

Further, the communication unit 102 is connected to the operating time measuring unit 50 for measuring the operating time of the air compressor, receives the operating time information of the air compressor, and is connected to the device information holding unit 60. It is possible to acquire device-specific information such as the model and number of the air compressor.

The maintenance defect diagnosis unit 104 determines the occurrence status of lubrication failure of the bearing of the motor 3 and the occurrence status of clogging of the suction filter 5 based on the measured values of various sensors 202, 204, 206 received via the communication unit 102. A maintenance failure detection step for detecting a maintenance failure event is executed by diagnosing a sign of a maintenance failure event such as a clogging occurrence status of the separator element 8. The maintenance failure diagnosis unit 104 detects the occurrence of a maintenance failure event by using the cluster center and the standard deviation calculated in advance using the sensor data when the air compressor is operating normally. The details will be described later.

The diagnostic model storage unit 106 stores information about the diagnostic model, including processing procedures and processing conditions when the maintenance failure diagnosis unit 104 executes diagnostic processing on the sensor measurement value. The details will be described later.

The failure risk estimation unit 108 searches the failure mode correspondence table 110 for the maintenance failure event diagnosed and detected by the maintenance failure diagnosis unit 104, and if the maintenance failure event is left unattended, the failure mode may occur over time. Perform the failure risk estimation process. The details will be described later.

When the failure mode correspondence table 110 is left unattended due to a maintenance failure event that is diagnosed and detected by the maintenance failure diagnosis unit 104, that is, a bearing lubrication failure of the motor 3, clogging of the suction filter 5, clogging of the separator element 8, and the like. It remembers the table associated with the failure mode that may occur. The details will be described later.

The maintenance effect estimation unit 112 recovers the maintenance failure event detected by the maintenance failure diagnosis unit 104 and the failure mode estimated by the failure risk estimation unit 108 in order to improve the failure mode and restore the normal state (maintenance cost). And the recovery time (maintenance time) is estimated, and the estimation estimation process is executed to calculate the failure occurrence probability when the maintenance measures for the maintenance failure event are implemented and when the maintenance measures are left unattended. Further, a display signal is output to the display unit 118 so that the maintenance failure event and the calculated recovery time, recovery cost, and failure occurrence probability are displayed on the display unit 118. The details will be described later.

In addition, the maintenance effect estimation unit 112 estimates maintenance measures for maintenance failure events and the work time and work cost required for the maintenance measures, and displays the estimated maintenance measures, work time, and work cost in comparison with the recovery time and recovery cost. The display signal to be displayed and the display signal to display the estimated maintenance measures along with the maintenance failure event are output.

The maintenance effect estimation unit 112 estimates the maintenance failure event, the recovery time and the recovery cost of the failure mode by referring to the statistical values calculated based on the past maintenance record data of the same model as the target device. Further, the maintenance effect estimation unit 112 calculates the ratio of the number of occurrences of the failure mode based on the past maintenance history data of the same model as the target device, and uses it as the failure occurrence probability.

The maintenance record data storage unit 114 uses the maintenance effect estimation unit 112 as a base for calculating an estimate, that is, the recovery cost for each failure mode, the recovery time information, and the failure occurrence rate for each failure mode based on the maintenance history. I remember information about. The details will be described later.

The input / output control unit 116 controls the output display of the estimation result calculated by the maintenance effect estimation unit 112 on the display unit 118, and records information such as maintenance history data input from the outside in the maintenance record data storage unit 114. To update.

The display unit 118 includes maintenance failure events based on the display signal output from the maintenance effect estimation unit 112, and the calculated recovery time, recovery cost, failure occurrence probability, maintenance measures for the maintenance failure events, and work required for the maintenance measures. Display time and work cost. The displayed screen is, for example, a screen as shown in FIGS. 8 and 9.

Next, the contents of the diagnostic model stored in the diagnostic model storage unit 106 will be described with reference to FIG. FIG. 3 is a diagram showing a configuration of a diagnostic model storage unit.

As shown in FIG. 3, the diagnostic model storage unit 106 stores the target sensor ID for each maintenance failure event, the content of the preprocessing, and the diagnostic processing parameters including the cluster center, standard deviation, and abnormality determination threshold value. Here, the target sensor ID is information for recognizing the sensor to be diagnosed.

In the air compressor, the types of sensor IDs are the ID (S1) of the vibration sensor 202 installed near the bearing of the motor 3, the ID (S2) of the differential pressure sensor 204 attached to the suction filter 5, and the separator element. The ID (S3) of the differential pressure sensor 206 attached to No. 8 is included. As for the vibration sensor S1, assuming a sensor capable of measuring the vibration acceleration in the three axial directions, the measured values of the respective vibration accelerations are S1 (1), S1 (2), and S1 (3).

For the pre-processing, the processing for converting from the sensor measurement value is described. For example, regarding poor lubrication of motor bearings, as a pretreatment, "effective values E1 (1), E1 (2), E1 (3)" are set, which are measured values of vibration acceleration in each axial direction S1 (1). , S1 (2) and S1 (3) are calculated to be E1 (1), E1 (2) and E1 (3), respectively.

The cluster center, standard deviation, and abnormality determination threshold are parameters in the diagnostic process described later. The cluster center and standard deviation are information indicating a normal range pre-learned based on normal data, and the abnormality determination threshold is a parameter for diagnosing deviation from this normal range.

Next, the contents of the diagnosis process of the maintenance failure diagnosis unit 104 will be described.

In poor maintenance diagnosis unit 104, _d 1 the sensor data of the N dimensions to be _diagnosed, d _{2, ...,} when the _{d N,} deviation distance _{L (d 1, d 2,} ..., d N) Equation 1 with respect to Execute the calculation process shown in.

Here, μ (d ₁ ), μ (d ₂ ), ... μ (d _N ) are the cluster centers, and σ (d ₁ ), σ (d ₂ ), ... σ (d _N ) represent the standard deviation. , Corresponds to the cluster center and standard deviation of the diagnostic model storage unit 106 in FIG. Further, the N-dimensional sensor data d ₁ , d ₂ , ..., D _{N to be} diagnosed are data after preprocessing and conversion based on the target sensor ID of the diagnostic model storage unit 106 in FIG. For example, in the case of diagnosis of poor lubrication of motor bearings, d ₁ = E1 (1), d ₂ = E1 (2), d ₃ = E1 (3), and in the case of diagnosis of suction filter clogging, d ₁ = It becomes S2.

The degree of deviation L (d ₁ , d ₂ , ..., D _N ) obtained by Equation 1 is a value calculated by how far the sensor data to be diagnosed is from the cluster center, that is, the normal reference value, and is a value calculated at normal times. It is expressed as a ratio to the standard deviation. For example, when the normal distribution is followed, when the degree of divergence is greater than 3, it can be determined to be abnormal, and when it is 3 or less, it can be determined to be normal. The abnormality determination threshold value of the diagnostic model storage unit 106 in FIG. 3 indicates this determination criterion, and it is possible to diagnose the occurrence status of each maintenance failure event from this magnitude relationship.

Next, the processing contents executed by the failure risk estimation unit 108 will be described with reference to the failure mode correspondence table 110 of FIG. FIG. 4 is a diagram showing the configuration of a failure mode correspondence table.

The failure risk estimation unit 108 searches for the failure mode that occurs over time when the maintenance failure event detected by the maintenance failure diagnosis unit 104 is left without performing maintenance by referring to the failure mode correspondence table 110 shown in FIG. Perform the processing to be performed.

For example, when the maintenance defect diagnosis unit 104 detects a motor bearing lubrication defect, the failure mode correspondence table 110 of FIG. 4 is referred to to search for a matching maintenance defect ID = 1. Then, the failure mode of maintenance failure ID = 1 is searched, and the progress level 1: "bearing failure" and the progress level 2: "motor failure" are extracted. Here, the progress level indicates the order of occurrence in time, and the smaller the number, the earlier the occurrence. That is, regarding poor lubrication of motor bearings, it means that if the state is left unattended, a bearing failure of progress level 1 will occur, and if this state is left unattended, a motor failure will occur. The progress level progresses to a failure mode in which the recovery cost and recovery time increase as the number increases. Further, although not shown here, the progress level = 0 indicates the maintenance failure event itself.

The failure risk estimation unit 108 transmits the search result in the above process to the maintenance effect estimation unit 112 and ends the process.

Next, the processing content of the maintenance effect estimation unit 112 will be described according to the processing flow shown in FIG. FIG. 5 is a diagram showing a processing flow of the maintenance effect estimation unit.

First, the maintenance effect estimation unit 112 determines whether or not the maintenance failure diagnosis unit 104 has detected a maintenance failure event (step S2000). If it is determined that it has been detected, a YES determination is made, and the process proceeds to the next processing step S2100. On the other hand, if it is determined that the detection has not been performed, a NO determination is made, and the processing step S2000 is repeated.

Next, the maintenance effect estimation unit 112 receives the failure mode estimation result for each progress level of the maintenance failure event from the failure risk estimation unit 108 (step S2100). That is, when "maintenance failure ID = 1 motor bearing lubrication failure" is detected as a maintenance failure event, the failure risk estimation unit 108 provides information on "progress level 1 bearing failure" and "progress level 2 motor failure". Get a medical examination from.

Next, the maintenance effect estimation unit 112 searches and extracts the estimated values regarding the recovery cost and recovery time for each estimated failure mode in the model from the maintenance record data storage unit 114 (step S2200). Here, regarding the model information, the maintenance record data storage unit 114 is searched based on the model / number included in the device-specific information received from the device information holding unit 60 via the communication unit 102 in the configuration of FIG.

FIG. 6 is an example of information on the recovery cost and the recovery time stored in the maintenance record data storage unit 114. As shown in FIG. 6, the maintenance record data storage unit 114 stores approximate values regarding the recovery cost and recovery time required to return to the normal state for each model type and failure mode. This is configured to correspond to the failure mode correspondence table shown in FIG.

As shown in FIG. 6, for example, "maintenance failure 1: motor bearing lubrication failure" is set as a failure mode, "progress level 0: maintenance failure (lubrication failure)", "progress level 1: bearing failure", "progress level". 2: There is a "motor failure", and there are "progress level 0: grease up", "progress level 1: bearing replacement", and "progress level 2: motor replacement" as maintenance work corresponding to each. Then, the recovery cost and recovery time required for each maintenance work are stored. These can be calculated by averaging based on the past maintenance history data of the same model, and the calculation results outside the monitoring device can be input via the input / output control unit 116. ing.

In step S2200 of FIG. 5, a process of searching the maintenance record data storage unit 114 and extracting the recovery cost and recovery time for each progress level for the target maintenance failure event is performed.

Next, the maintenance effect estimation unit 112 performs a process of extracting information on the failure occurrence rate when the maintenance failure event is left unattended in the model from the maintenance record data storage unit 114 as the failure occurrence probability (step S2300).

FIG. 7 is a graph showing an outline of information regarding the failure occurrence rate stored in the maintenance record data storage unit 114. As shown in FIG. 7, the maintenance record data storage unit 114 stores information on the failure occurrence rate as shown here for each model type and failure mode. This information is the result of calculating the ratio of the number of failures to the whole based on the maintenance history data of the same model. Since this ratio varies depending on the maintenance implementation status and cumulative operating time of each individual, it is divided into equipment that is regularly maintained and equipment that is not, and each is aggregated for each cumulative operating time. doing.

For example, for "failure mode: motor bearing failure", the grease-up work is calculated separately for the equipment that is performed less than once every 3000 hours recommended by the manufacturer and the equipment that is not performed. Here, "equipment that performs grease-up work at least once every 3000 hours" is regarded as "equipment that performs regular maintenance", and "grease-up work is performed less than once every 3000 hours". "Equipment that is carried out frequently" is regarded as "equipment that is not regularly maintained".

By using this information, the probability of failure occurrence when the target device does not perform the grease-up work can be roughly referred to as the failure rate of "equipment that is not regularly maintained". The probability of failure occurrence when the grease-up work is carried out is roughly the same, and the failure occurrence rate of "equipment that is regularly maintained" can be referred to. At this time, data on the cumulative operating time of the target device is required, and as the information on the cumulative operating time, the data received from the operating time measuring unit 50 can be used via the communication unit 102 shown in FIG. Then, the failure rate of the maintenance-executed device and the failure rate of the unmaintained device with respect to the cumulative operating time of the device are obtained, and each is replaced and interpreted as the failure occurrence probability.

Next, the maintenance effect estimation unit 112 sends the input / output control unit 116 the information on the recovery cost / recovery time for each estimated failure mode obtained in step S2200 and the information on the failure occurrence probability when the maintenance failure is left unattended obtained in step S2300. It is output to the display unit 118 via the display unit 118 and displayed on the display unit 118 (step S2400). After this, the process ends.

Next, an example of the display screen on the display unit 118 of the monitoring device 100 will be described with reference to FIG. When the maintenance failure diagnosis unit 104 detects the occurrence of a maintenance failure event, as shown in FIG. 8, displays 801A and 801B prompting the execution of maintenance work necessary for returning to normal for the maintenance failure event are displayed.

For example, when an event of motor bearing lubrication failure is detected, as shown in FIG. 8, along with the display 801A indicating the maintenance failure event, "progress" in "maintenance failure 1: motor bearing lubrication failure" shown in FIG. Display 801B prompting to carry out the work of "level 0: grease up".

In addition, a display 802 that can compare the failure occurrence probability when this maintenance work (grease up) is performed and when it is left unexecuted is displayed, and the recovery cost and recovery time, the work time required for maintenance measures, and the work cost are displayed. The display 803 that can be compared is displayed.

The failure occurrence probability is displayed by comparing in the form of a bar graph using the failure occurrence rate (probability) data of the maintenance-executed / unmaintained equipment for each failure mode received from the maintenance effect estimation unit 112. For example, in the case of "motor bearing lubrication failure", there are "bearing failure" and "motor failure" as failure modes. Therefore, when maintenance is performed, that is, when grease is applied, the data display of the failure occurrence probability is 802A. And the display 802B of the data of the failure occurrence probability when the maintenance is not performed, that is, when the product is left without grease-up, is displayed side by side for comparison.

Further, the display unit 118 displays the display 803B in the form of a bar graph showing the data related to the recovery cost and recovery time required for the maintenance work for each failure mode received from the maintenance effect estimation unit 112, and the work cost and work time required for the maintenance work. The data related to the above is displayed in comparison with the display 803A shown in the form of a bar graph.

The display screen displayed on the display unit 118 is not limited to the form shown in FIG. Other forms will be described with reference to FIG.

As shown in FIG. 9, along with the display 901A displaying the maintenance failure event, the display prompting to perform the work of "progress level 0: grease up" in "maintenance failure 1: motor bearing lubrication failure" shown in FIG. Display 901B.

In addition, a display 902 that can compare the probability of failure occurrence when grease-up is performed and a case where it is left unattended, and a display 903 that can compare the recovery cost and recovery time, the work time required for maintenance measures, and the work cost are displayed. ..

In FIG. 9, the failure occurrence probability is the case where the maintenance work is performed and the case where the maintenance work is performed using the data of the failure occurrence rate (probability) of the maintenance-executed / unmaintained equipment for each failure mode received from the maintenance effect estimation unit 112. If not, the failure occurrence probability data is divided into a certain range and "A" to "D" are displayed. In FIG. 9, “A” is displayed as the probability of occurrence is higher, and “D” is displayed as the probability of occurrence is lower.

Similarly, regarding the recovery cost and recovery time, when the maintenance work is performed and when the maintenance work is not performed using the data on the recovery cost and recovery time for the maintenance work for each failure mode received from the maintenance effect estimation unit 112. The cost and time data of the above are divided into a certain range and displayed as "A" to "D". In FIG. 9, “A” is displayed as the cost and time increase, and “D” is displayed as the cost and time increase.

Further, as shown in FIG. 9, when there are some candidates for the failure mode that may occur due to the neglect of maintenance failure, it is possible to preferentially display the failure mode having the highest influence. .. By performing such a display, it is possible to obtain an effect that the user can more easily understand the cost and time reduction merit of performing maintenance.

For example, as shown in the "motor replacement" time column in display 903 of FIG. 9, items with a large cost or time loss may be highlighted to distinguish them from other displays, or "bearing failure" in display 902. As shown in the column of, a failure mode having a very high occurrence probability can be displayed separately from other failure modes by highlight display. It is also possible to emphasize the display of maintenance measures 801B and 901B.

It is also possible to allow the user to display only the failure probability, display only the cost / time, select the number of display items of the failure mode, and the like by inputting an input device (not shown) or the like.

Next, the effect of this embodiment will be described.

The monitoring device 100 of the first embodiment of the present invention described above is a maintenance failure event of the air compressor based on the sensor data measured by the vibration sensor 202, the differential pressure sensor 204, and the differential pressure sensor 206 attached to the air compressor. The maintenance failure diagnosis unit 104 that detects the failure mode, and the failure risk estimation unit 108 that estimates the failure mode that occurs over time when the maintenance failure diagnosis unit 104 detects the maintenance failure event and leaves the maintenance failure event unattended. It estimates the recovery time and recovery cost required to improve the failure mode based on the maintenance failure event and restore it to normal, and outputs it as a display signal to display the estimated recovery time and recovery cost and the maintenance failure event. A maintenance effect that calculates the failure occurrence probability of the failure mode when maintenance measures are implemented and not implemented for maintenance failure events, and outputs the calculated failure occurrence probability and maintenance failure event as a display signal. It includes an estimation unit 112. Then, by such a monitoring device 100, maintenance failure detection that detects a maintenance failure event of the air compressor based on the sensor data measured by the vibration sensor 202, the differential pressure sensor 204, and the differential pressure sensor 206 attached to the air compressor. The process, the failure risk estimation process that estimates the failure mode that occurs over time when the maintenance failure event is left unattended when the maintenance failure event is detected in the maintenance failure detection process, and the failure mode based on the maintenance failure event Estimate the recovery time and recovery cost required for improvement and normal recovery, display the estimated recovery time and recovery cost and maintenance failure event on the display unit 118, or implement maintenance measures for maintenance failure events. An estimation estimation step of calculating the failure occurrence probability of the failure mode in the case where the failure occurs and the case where the failure occurs and displaying the calculated failure occurrence probability and the maintenance failure event on the display unit 118 is performed.

With the above configuration, when the occurrence of a maintenance failure event is detected, not only is it encouraged to carry out maintenance work for normal recovery, but also the risk of neglecting without performing maintenance work is reduced to the probability of failure occurrence and recovery. It can be displayed in an easy-to-understand manner by comparing in the form of cost and recovery time, and the user benefits of performing maintenance work can be presented in an easy-to-understand manner. Since the user can easily understand the necessity of early maintenance work and the timing of its execution by such presentation, it is possible to replace consumable parts and perform daily maintenance work at an appropriate timing, resulting in loss in the future. It becomes possible to prevent the occurrence of the above, and the effect can be obtained.

In addition, the maintenance effect estimation unit 112 estimates the maintenance measures for the maintenance failure event, estimates the work time and the work cost required for the maintenance measures, and displays the estimated work time and the work cost in comparison with the recovery time and the recovery cost. Since the display signal to be displayed is output, the merit of performing the maintenance work can be presented to the user in an easy-to-understand manner.

Further, the maintenance effect estimation unit 112 estimates the recovery time and recovery cost of the maintenance failure event and the failure mode by referring to the statistical values calculated based on the past maintenance record data, so that the failure mode and the recovery time can be estimated. The estimation accuracy of the above can be improved, and the necessity of more accurate maintenance work can be presented.

In addition, the maintenance effect estimation unit 112 estimates the maintenance failure event and the recovery time and recovery cost of the failure mode by referring to the statistical values calculated based on the past maintenance record data of the same model as the target device. Similarly, the accuracy of estimating the failure mode and the recovery time can be improved, and the necessity of more accurate maintenance work can be presented.

Further, the maintenance effect estimation unit 112 estimates the maintenance measures for the maintenance failure event, and outputs a display signal for displaying the estimated maintenance measures along with the maintenance failure event, thereby performing the maintenance work in the same manner. The merits can be presented to the user in an easy-to-understand manner.

Further, the maintenance effect estimation unit 112 can improve the calculation accuracy of the failure occurrence probability by calculating the ratio of the number of occurrences of the failure mode based on the past maintenance history data and using it as the failure occurrence probability, which is more accurate. It will be possible to present the necessity of various maintenance work.

Further, the maintenance effect estimation unit 112 calculates the failure mode occurrence ratio based on the past maintenance history data of the same model as the target device and uses it as the failure occurrence probability, thereby similarly calculating the failure occurrence probability. The accuracy can be improved, and the necessity of more accurate maintenance work can be presented.

In addition, the maintenance failure diagnosis unit 104 is more accurate by detecting the occurrence of a maintenance failure event using the cluster center and standard deviation calculated in advance using the sensor data when the air compressor is operating normally. It becomes possible to detect the occurrence of various maintenance failure events, and it becomes possible to present the necessity of more accurate maintenance work.

<Example 2>
The industrial equipment monitoring device and the monitoring method of the industrial equipment according to the second embodiment of the present invention will be described with reference to FIGS. 10 and 11. The same reference numerals are given to the same configurations as those of the monitoring device 100 of the first embodiment, and the description thereof will be omitted.

As shown in FIG. 10, the monitoring device 100A of this embodiment communicates with the production control system 1000 via the communication unit 102A, and acquires the production plan of the product planned and stored in the production control system 1000. To do.

In the monitoring device 100A, the communication unit 102A receives the production plan information from the production control system 1000. Then, the maintenance effect estimation unit 112A analyzes the operation prospect of the production line in which the air compressor is used from the production plan information, and determines whether the necessity of maintenance work is high or low in terms of production control.

If the production plan for the next week is in place and it is determined that the loss due to the failure without maintenance work is very high and that the failure may cause an external disadvantage to the user. , Prompt for early maintenance "Next week, there will be a delivery date for a large number of customers' orders. Maintenance measures by early grease-up are required." Display 1104, display 1101A indicating a maintenance failure event, and maintenance work will be carried out. Display 1101B is displayed on the display unit 118A together with a display 1101B for urging the user, a display 1102 for comparing the failure occurrence probability, a display 1103 for comparing the recovery cost and the recovery time, and the work time and the work cost required for maintenance measures.

Other configurations and operations of the monitoring device 100A are substantially the same as those of the monitoring device 100 of the first embodiment described above, and details thereof will be omitted.

The industrial equipment monitoring device and the industrial equipment monitoring method of the second embodiment of the present invention also have substantially the same effects as the industrial equipment monitoring device and the industrial equipment monitoring method of the first embodiment described above.

In addition, by further displaying the problems in production control when the maintenance failure event is left unattended on the display unit 118A, the necessity of maintenance work can be presented to the user in an easy-to-understand manner from another angle. , It is possible to support that appropriate maintenance work is performed at a more appropriate timing.

<Others>
The present invention is not limited to the above examples, and various modifications and applications are possible. The above-described examples have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the described configurations.

For example, each part of the monitoring devices 100 and 100A does not necessarily have to be in the same place, and each part may be in a different place as long as communication is possible, or a plurality of parts are in the same place and the others are different. It may be in the place of. For example, the display units 118 and 118A may be provided not only at the user's production site but also at a monitoring post of a manufacturer that produces industrial equipment other than the production site or a manufacturer that undertakes maintenance. Further, only the communication units 102 and 102A may be provided at the production site, and the other units may be provided at the monitoring post.

1 ... Compressor body 2 ... Belt 3 ... Motor 4 ... Inverter 5 ... Suction filter 6 ... Suction throttle valve 7 ... Oil tank 8 ... Separator element 9 ... Pressure regulating valve 10 ... Check valve 11 ... Aftercooler 12 ... Oil cooler 13 ... Oil filter 14 ... Temperature control valve 15 ... Cooling fan 17 ... Control device 30 ... Lubricating oil 50 ... Operating time measurement unit 60 ... Equipment information holding unit 100, 100A ... Monitoring device (industrial equipment monitoring device)
102, 102A ... Communication unit 104 ... Maintenance defect diagnosis unit (maintenance defect detection unit)
106 ... Diagnostic model storage unit 108 ... Failure risk estimation unit 110 ... Failure mode correspondence table 112, 112A ... Maintenance effect estimation unit (maintenance cost estimation unit, failure occurrence probability estimation unit)
114 ... Maintenance record data storage unit 116 ... Input / output control unit 118, 118A ... Display unit 202 ... Vibration sensor 204 ... Differential pressure sensor 206 ... Differential pressure sensor 208 ... Pressure sensor 1000 ... Production management system

Claims (12)

A maintenance defect detection unit that detects maintenance failure events of the industrial equipment based on sensor data measured by various sensors attached to the industrial equipment.
When the maintenance failure detection unit detects the maintenance failure event, a failure risk estimation unit that estimates a failure mode that occurs over time when the maintenance failure event is left unattended, and a failure risk estimation unit.
The recovery time and recovery cost required to improve the failure mode based on the maintenance failure event and restore the normal state, the maintenance measure for the maintenance failure event, and the work time and work cost required for the maintenance measure are estimated and estimated. It is provided with a maintenance cost estimation unit that displays the recovery time, the recovery cost, and the maintenance failure event, and outputs the estimated work time and work cost as a display signal for comparison with the recovery time and the recovery cost. An industrial equipment monitoring device characterized by the cost. In the industrial equipment monitoring device according to claim 1,
The maintenance cost estimation unit estimates the maintenance failure event and the recovery time and recovery cost of the failure mode by referring to statistical values calculated based on past maintenance record data. apparatus. In the industrial equipment monitoring device according to claim 2.
The maintenance cost estimation unit estimates the maintenance failure event and the recovery time and recovery cost of the failure mode by referring to statistical values calculated based on past maintenance record data of the same model as the target device. A featured industrial equipment monitoring device. In the industrial equipment monitoring device according to claim 1,
As a display signal for calculating the failure occurrence probability of the failure mode when the maintenance measure is implemented and not implemented for the maintenance failure event, and displaying the calculated failure occurrence probability and the maintenance failure event. An industrial equipment monitoring device that is further equipped with a failure occurrence probability estimation unit that outputs. In the industrial equipment monitoring device according to claim 4,
The failure occurrence probability estimation unit is an industrial equipment monitoring device that estimates maintenance measures for the maintenance failure event and outputs a display signal that displays the estimated maintenance measures alongside the maintenance failure event. In the industrial equipment monitoring device according to claim 4,
The failure occurrence probability estimation unit is an industrial equipment monitoring device characterized in that it calculates the ratio of the number of occurrences of the failure mode based on past maintenance history data and uses it as the failure occurrence probability. In the industrial equipment monitoring device according to claim 6,
The failure occurrence probability estimation unit is an industrial equipment monitoring device characterized in that the failure occurrence probability estimation unit calculates the ratio of the number of occurrences of the failure mode based on the past maintenance history data of the same model as the target device and uses it as the failure occurrence probability. In the industrial equipment monitoring device according to claim 1,
The maintenance failure detection unit is characterized in that it detects the occurrence of the maintenance failure event by using the cluster center and standard deviation calculated in advance using the sensor data when the industrial equipment is operating normally. Equipment monitoring device. In the monitoring device for industrial equipment according to claim 1.
The industrial equipment is an air compressor,
An industrial equipment monitoring device characterized in that the maintenance failure event includes any one of poor lubrication of a motor bearing, clogging of a suction filter, and clogging of a separator element. In the industrial equipment monitoring device according to claim 9.
An industrial equipment monitoring device characterized in that a vibration sensor and a differential pressure sensor are used as the sensor. In the industrial equipment monitoring device according to claim 1,
An industrial equipment monitoring device characterized by further equipped with a display unit that displays based on the output display signal. In the industrial equipment monitoring device according to claim 11,
An industrial equipment monitoring device characterized in that problems in production control when the maintenance failure event is left unattended are further displayed on the display unit.

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