JP7500898B2 - Material handling equipment part replacement prediction system and part replacement prediction method - Google Patents

Description

The present invention relates to a system and method for predicting part replacement for material handling equipment installed in, for example, an automated warehouse in a logistics center.

In logistics centers for the distribution industry and the like, there are installed automated warehouses equipped with a storage warehouse section for storing cases (storage cases) containing products, and a picking station for picking the required number of products from the cases removed from the storage warehouse section and sending them to the shipping line. In addition, downstream of the picking station, there may be a packaging line for packaging the picked products using an automatic box sealing device or the like.

Automated warehouses are managed by a WMS (Warehouse Management System) equipped with a host computer (or server, cloud, etc.) that manages all warehouse operations, and a WCS (Warehouse Control System) that incorporates a computer connected to the WMS host computer.

Based on instructions from the WMS, the WCS issues work instructions such as storage, transportation, retrieval, picking, and automatic sealing to the material handling equipment (transportation devices, lifting devices, buffer conveyors, etc.) in the automated warehouse, as well as the picking stations and packaging lines, via wireless remote control and communication lines.

Japanese Patent Application Laid-Open No. 2009-115606

In the logistics center mentioned above, if a malfunction occurs in the material handling equipment (transport devices, lifting devices (lifters), buffer conveyors, etc.) in the automated warehouse, causing the transportation of goods and the storage and retrieval of goods to stop, it will take a long time to restore the transport system, and goods may not arrive at the stores or other locations in time, resulting in significant losses.

For this reason, logistics centers regularly inspect and repair the material handling equipment in their automated warehouses, and depending on the type of malfunction, it may be necessary to stop the operation of the material handling equipment to inspect and repair it. These inspections and repairs need to be carried out quickly, but depending on the scale and type of malfunction, it may take time, resulting in a long downtime for the material handling equipment, or it may become unclear when repairs will be completed. Such cases cause great inconvenience to the operators of the logistics center, so it is important to detect signs of malfunctions in material handling equipment in advance to prevent such situations from occurring.

On the other hand, the efficiency of logistics at logistics centers is thought to be promoted by improving efficiency in terms of manpower and time, and to achieve this, the following are necessary:
・Can be done by fewer workers (responding to labor shortages) ・Embodying a system that does not make workers walk, make mistakes, or put a burden on them ・No special skills are required, anyone can do it ・High reliability that allows it to operate 24 hours a day Reducing manpower through automation would be counterproductive if the possibility of it stopping due to an accident increases. In other words, if the transport system were to stop, it would have an enormous impact on business, so in order to achieve these goals, "unstoppable logistics" is necessary.

However, to achieve "uninterrupted logistics," it is believed that stable operation of the transport system is fundamentally necessary. The conventional idea of "preventive maintenance" was that this requires regular inspections of material handling equipment to find any abnormalities and to replace parts appropriately.

However, it was unavoidable that it took time and money for workers to discover abnormalities in material handling equipment, and specialized knowledge was also required.

In particular, the lifting device that moves the storage cases vertically, which is one of the material handling equipment, is installed in a small space in the storage warehouse that is difficult for maintenance workers to access, making it difficult to carry out regular inspections. This makes it difficult to manually find faults or predict when parts need replacing.

In view of the problems with the conventional technology, the present invention aims to provide a system and method for predicting part replacement for material handling equipment, which makes it possible to predict the time for part replacement before a failure occurs.

In order to solve the above problems, the inventor came to the realization that in order to be able to predict when to replace parts, it is necessary to systematize the detection of abnormalities before a failure occurs, and that in order to do so, it is necessary to build a mechanism for converting "normal operation" and "abnormal operation" into data and predicting failures in advance. This led to the idea of utilizing artificial intelligence (AI) technology and IoT technology as the technologies to achieve this.

That is, the part replacement prediction system for material handling equipment according to the first aspect of the present invention has a configuration that predicts the replacement time of a part of the material handling equipment by artificial intelligence (AI) technology using the output from a sensor installed in the material handling equipment. More specifically, it can be embodied as a part replacement prediction system for material handling equipment that predicts the replacement time of the part by attaching a vibration sensor to the material handling equipment of an automated warehouse and using artificial intelligence (AI) technology that uses a learned model of the relationship between the amount of change over time in the frequency of vibration generated from the parts that make up the material handling equipment and the degree of abnormality of the parts.

In this application, material handling equipment refers to equipment that assists in transportation and loading and unloading operations, and is a concept that includes not only the above-mentioned transport devices, lifting devices (lifters), and buffer conveyors, but also forklifts, pallets, and general conveyors as work machines used in particular to make logistics operations more efficient.

As a part replacement prediction system for material handling equipment according to the second aspect of the present invention, in the first aspect, the material handling equipment may be embodied as a lifting device installed in the storage warehouse section of the automated warehouse.

As a part replacement prediction system for material handling equipment according to a second aspect of the present invention, as an aspect different from the above, in the first aspect, the material handling equipment may be embodied as a stacker crane type transport device installed in the storage warehouse section of the automated warehouse.

As a part replacement prediction system for material handling equipment according to a third aspect of the present invention, in either the first or second aspect, the sensor may be a vibration sensor that detects vibrations of the material handling equipment, and the artificial intelligence technology may be configured to create a learned model of the relationship between the amount of change over time in the frequency of the vibrations of the material handling equipment and the degree of abnormality of the part.

As a part replacement prediction system for material handling equipment according to the fourth aspect of the present invention, in the third aspect, the sensor may be configured to analyze vibrations generated from multiple parts constituting the material handling equipment using an FFT analyzer, thereby decomposing the vibrations into frequency components of the parts constituting the material handling equipment.

The part replacement prediction system for material handling equipment according to the fifth aspect of the present invention may be embodied in the third or fourth aspect such that the vibration sensor is attached to the drive system of the material handling equipment.

The sixth aspect of the present invention relates to a system for predicting part replacement of material handling equipment, and in the fifth aspect, the drive system may be embodied as one including a configuration including active rotation means and passive rotation means and fixing means for fixing them, for example, a motor and pulleys, and bolts for fixing them.

As a part replacement prediction system for material handling equipment according to the seventh aspect of the present invention, in any of the first to sixth aspects, the sensor may be configured to be retrofitted to an existing material handling equipment in which the sensor is not installed.

In order to solve the above problems, the method for predicting part replacement for material handling equipment according to the eighth aspect of the present application includes a step of detecting vibrations generated from the material handling equipment using a vibration sensor installed in the material handling equipment, a step of decomposing the vibrations into frequencies by analyzing the vibrations with an FFT analyzer, and a step of predicting the time to replace the part using artificial intelligence (AI) technology that uses the relationship between the amount of change in the frequency over time and the degree of abnormality of the material handling equipment as a learned model.

According to the present invention, by utilizing artificial intelligence (AI) technology and IoT technology to detect abnormalities in parts before a breakdown occurs in the lifting device and predict when to replace the parts, it is possible to realize "predictive maintenance" = "non-stop logistics." This also makes it possible to reduce total costs.

FIG. 1 is a conceptual diagram showing an example of an automated warehouse installed in a logistics center. FIG. 2 is a perspective view showing a part of the storage warehouse section of the automated warehouse shown in FIG. FIG. 2 is a block diagram showing a system configuration of a WMS and a WCS that manage the automated warehouse shown in FIG. 2 is a perspective view of an example of a storage case stored in a storage warehouse section of the automated warehouse shown in FIG. 1 . FIG. 2 is a schematic diagram showing a main part of a lifting device installed in a storage warehouse section of the automated warehouse shown in FIG. 1 . 6 is an enlarged schematic view of a portion of the lifting device shown in FIG. 5 . 1 is a graph showing an example of a frequency for each part obtained by analyzing a vibration waveform measured by a vibration sensor using an FFT analyzer. 1 is a graph showing an example of a frequency for each part obtained by analyzing a vibration waveform measured by a vibration sensor using an FFT analyzer. 2 is a graph illustrating the concept of the part replacement prediction system according to the present embodiment. 1 is a conceptual diagram of an example of a part replacement prediction system according to an embodiment of the present invention. An example in which the part replacement prediction system according to the present embodiment is applied to detection of loose bolts will be described.

The following describes a system for predicting part replacement in an elevator device according to one embodiment of the present invention, with reference to the drawings. Note that the following shows a schematic view of the scope necessary for the explanation of achieving the object of the present invention, and mainly describes the scope necessary for the explanation of the relevant parts of the present invention. The parts that are omitted from the explanation are based on publicly known technology.

Figure 1 is a conceptual diagram showing an example of an automated warehouse installed in a logistics center, Figure 2 is a perspective view showing a portion (one row of shelves) of the storage warehouse section of the automated warehouse shown in Figure 1, and Figure 3 is a block diagram showing the system configuration of the WMS and WCS that manage the automated warehouse shown in Figure 1.

The automated warehouse 1 in this embodiment includes a storage warehouse section (warehouse means) 100, a picking station 200, and a packaging means 300.

The storage warehouse section 100 has multiple rows of shelves 10, with multiple tiers with storage space arranged along the aisles, multiple aisles 20 arranged adjacent to each shelf 10, and a conveying device 30 provided in each aisle 20 and moving along the aisles 20. Note that the aisles 20 may be arranged between the shelves 10.

The conveying device 30 is, for example, a device that employs a stacker crane system that moves by simultaneously combining horizontal and/or vertical movement in the aisle 20, and supports the storage case (storage section) 40 containing the product item 70 with an extendable arm or the like, and stores it on the shelf 10 or removes it from the shelf 10.

The storage warehouse section 100 further includes a lifting device 50 arranged at the end of each aisle 20, and a buffer conveyor 60 arranged at the end of each tier of the shelves 10. The lifting device 50 is a device that places the storage cases 40 on a stand 55 and transports them vertically, and the buffer conveyor 60 is a conveyor that transfers the storage cases 40 between the transport device 30 and the lifting device 50.

The automated warehouse 1 is managed by a WMS (warehouse management system) 400 equipped with a host computer (or server, cloud, etc.) that centrally manages warehouse operations, and a WCS (warehouse control system) 500 that incorporates a computer connected to the WMS 400 host computer.

As shown in FIG. 3, WCS500 has a computer equipped with a CPU, main memory, and an external interface, and is connected to the host computer of WMS400, and storage connected to this computer via the external interface.

Based on instructions from the WMS 400, the WCS 500 issues work instructions such as storage, transportation, retrieval, picking, and automatic sealing to the material handling equipment (transportation device 30, lifting device 50, buffer conveyor 60, etc.) in the automated warehouse 1, the picking station 200, and the packaging means 300, etc., via wireless remote control or communication lines.

As shown in FIG. 4, inside each storage case 40 stored at any opening of the shelf 10 in the storage warehouse section 100, one or more product items 70 are stored, each having a first identifier 71 made of a barcode. In addition, on the outside of the storage case 40, a second identifier 41 made of a barcode that can be associated with the first identifier 71 of the product item 70 is provided.

The first identifier 71 is used to identify the product item 70 and is different for each product. The second identifier 41 is used to identify the storage case 40 and is different for each storage case 40. The first identifier 71 and the second identifier 41 may be configured as an identifier other than a barcode, such as a two-dimensional code or an RFID.

The attribute data (quantity, date, shipping/receiving destination, storage location, weight, etc.) of all product items 70 stored in the storage case 40 is stored in association with the first identifier of the product item and the second identifier of the storage case 40 storing the product item, and is managed by the WMS 400 and WCS 500 described above.

Although not shown in the figures, the logistics center equipped with the automated multi-level warehouse 1 is equipped with a truck berth where product items 70 from each manufacturer arrive. When the product items 70 from each manufacturer arrive at the truck berth, a traying process is carried out in order to unpack each product item 70 from its packaging container such as a cardboard box and store it in the storage warehouse section 100. The traying process is a process in which the first identifier 71 attached to each product item 70 is linked (associated as described above) to the second identifier 41 attached to the storage case 40.

The second identifier 41 attached to the storage case 40 is read when the storage case 40 passes in front of a barcode reader installed in the traying work area. The second identifier 41 is read using a known technique, such as irradiating an LED light source and receiving the reflected light with a photodiode. Meanwhile, the first identifier 71 attached to the product item 70 is read by a handy scanner installed at the product input location of the truck berth every time the product item 70 is placed in the storage case 40.

Then, the computer of the WCS 500 creates a traying table based on the first identifiers 71 and their quantities and the second identifiers 41 that have been read, and stores this in a memory area for traying tables in the storage. This allows the WCS 500 to know the names and quantities of the product items 70 stored in each storage case 40.

After the above-mentioned traying process is completed, the storage cases 40 are transported to the storage warehouse section 100 by the receiving conveyor 600 (see FIG. 2). Note that one end of the receiving conveyor 600, which connects the traying process area with the storage warehouse section 100, is located above the picking station 200, so in FIG. 1, the receiving conveyor 600 is omitted from the illustration in order to make it easier to see the configuration of the picking station 200.

When storing a storage case 40 in the storage warehouse section 100, a storage command issued by the host computer of the WMS 400 is first transmitted to the computer of the WCS 500.

The CPU of the WCS 500 grasps the names and quantities of the product items 70 to be stored in the storage warehouse section 100 by the above-mentioned storing command. The CPU of the WCS 500 compares the storage data table and the storing table based on a predetermined work instruction program stored in the hard disk drive, creates optimal storing order information, and issues work instructions to the material handling equipment (transport device 30, buffer conveyor 60, lifting device 50, etc.) of the storage warehouse section 100. Here, the storing order information refers to the route and order that determines the order and the width of each storage case 40 to be stored.

At this time, the CPU of the WCS 500 refers to the inventory table and the traying table to confirm in which storage case 40 each product item 70 is stored, and then compares the storage information table with the traying table to determine which shelf 10 and which level each storage case 40 should be stored.

When determining the address (indicating the address of the frontage of the tier of shelves 10 in aisle 20) for storing a storage case 40, the address information of the storage case 40 that has already been stored is read from the storage data table, and the storage case 40 is stored in the tier of shelves 10 in the aisle 20 where the number of storage cases 40 is small.

Each storage case 40 for which a storage command has been issued is moved to the shelf 10 of the aisle 20 in the order determined by the operation of the material handling equipment, and is stored there. The WCS 500 then updates the storage data table according to this storage data (address information of the storage case 40, storage time, etc.).

When an order is placed by a customer of a store or the like for a product item 70 stored in any opening of the shelf 10 in this manner, the storage case 40 containing the product item 70 is removed from the opening of the shelf 10 by the transport device 30 and transported to the picking station 200 via the buffer conveyor 60 and the lifting device 50.

When a product item 70 is to be shipped from the storage warehouse section 100, a shipping command for the product item 70 is first issued from the WMS 400, and this command is transmitted to the picking station 200 via the computer of the WCS 500. The shipping command contains shipping information that associates the product item 70 with its quantity, shipping time, and delivery destination.

The CPU of the WCS 500 compares the storage information and the shipping information according to a predefined work instruction program stored in the storage, creates optimal shipping order information, and operates the material handling equipment (the transport device 30, the buffer conveyor 60, the lifting device 50, etc.).

Specifically, the WCS 500 selects a storage case 40 containing a product item 70 corresponding to the out-of-stock command according to a default work instruction program stored in the storage, and moves the transport device 30 to take the storage case 40 out of the shelf 10. When the transport device 30 arrives at the storage location of the storage case 40 to be taken out, it grasps the storage case 40 and transfers it to the buffer conveyor 60.

When a storage case 40 released from the storage warehouse section 100 arrives at the picking station 200, a picking section 90 such as a worker or robot picks the product item 70 according to instructions on a display screen that shows the product items 70 and their quantities in the storage case 40, and places the product items 70 in shipping cases (shipping media) 80 (or packaging materials such as cardboard) for each shipping destination that travels along a shipping conveyor 91.

When the placement of the product items 70 in the shipping case 80 is complete, the picking section 90 touches the picking complete button displayed on the display screen. This causes the WCS 500 to transport the shipping case 80 toward the packing section 300 according to the default work instruction program stored in the storage.

Of the storage cases 40 from which product items 70 have been picked at the picking station 200, those with product items 70 remaining inside are transported again to the storage warehouse section 100 by the buffer conveyor 60 and the lifting device 50, and are returned to the shelf 10 by the transport device 30. In this case, the location to which the storage case 40 is returned does not have to be the original location, and any available storage space on any shelf 10 can be used (free location method).

Meanwhile, once the picking of the product items 70 has been completed and the storage case 40 is empty, it is transported to the traying work area upstream of the storage warehouse section 100, and after the newly arrived product items 70 have been stored, it is transported to the storage warehouse section 100 via the receiving conveyor 600.

In the packaging department 300, the product items 70 are removed from the shipping cases 80 sent from the picking station 200, packaged in packaging materials such as cardboard boxes, and then sent for shipping and delivery.

The shipping case 80 sent to the packing department 300 is checked for discrepancies between the shipping information read from the storage of the WCS 500 and information such as the product items 70 and their quantities in the shipping case 80, customer information and store information of the shipping destination, and shipping date and time.The product items 70 are then removed from the shipping case 80 and packed using an automatic box-sealing device, etc.

In the automated warehouse 1 where the above-mentioned operations are carried out, if a malfunction occurs in the material handling equipment (transport device 30, lifting device 50, buffer conveyor 60, etc.) in the storage warehouse section 100 and the transportation or storage and retrieval of product items 70 stops, the arrival time to customers may be delayed, resulting in significant losses.

In particular, the lifting device 50, among the material handling equipment of the storage warehouse section 100, is installed in a narrow space within the storage warehouse section 100 (Figure 1). Therefore, when the material handling equipment operates 24 hours a day for a long period of time, such as in the automated warehouse 1 of a logistics center, it is difficult for maintenance workers to enter the storage warehouse section 100 and perform regular inspection work of the lifting device 50. In addition, the lifting device 50 is equipped with pulleys and belts that serve as a power transmission mechanism for vertically moving the stand 55 on which the storage case 40 is placed up and down, but since the bearings built into the pulleys cannot be visually inspected from the outside, it is extremely difficult to detect the degree of abnormality of the bearings or the time to replace them by manpower alone.

Therefore, the following describes the part replacement prediction system for the lifting device 50 according to this embodiment, with reference to the drawings.

Figure 5 is a schematic diagram showing the main parts of the lifting device 50 installed in the storage warehouse section 100, and Figure 6 is a schematic diagram showing an enlarged view of a portion (near the upper pulley) of the lifting device 50 shown in Figure 5.

The lifting device 50 includes a drive system (driving parts) including a lower pulley 51 and an upper pulley 52, which are passive rotation means, a belt 53 suspended between them, a motor with a speed reducer 54, which is an active rotation means that drives the rotation of the lower pulley 51, and a stand 55 that moves up and down in the vertical direction in conjunction with the rotation of the upper pulley 52. The lower pulley 51 and the motor with a speed reducer 54 are fixed onto a stage 57 by bolts 56a and 56b (fixing means), and the upper pulley 53 is fixed to a frame 59 by a bolt 58 (fixing means).

A vibration sensor 2 that detects vibrations generated by each of the above-mentioned components (lower pulley 51, upper pulley 52, motor with speed reducer 54, bolts 56a, 56b, 58) that make up the drive system of the lifting device 50 is attached near each of the above-mentioned components. A wireless vibration sensor that can automatically measure vibration levels periodically and transmit the measured values to a higher-level device such as the WCS 500 by wireless communication is suitable as the vibration sensor 2.

Each of the above components that make up the lifting device 50 has its own resonant frequency (natural frequency). The vibration sensor 2 simultaneously measures the vibrations (acceleration) in three axes (X, Y, and Z directions) generated by each component, and analyzes the complex vibration waveform that is the combination of all these vibrations with an FFT (Fast Fourier Transformation) analyzer to break it down into simple frequency components for each component that is the source of the vibration.

Meanwhile, as each component that makes up the lifting device 50 deteriorates over time (loosening in the case of bolts), the magnitude and frequency of the vibration generated by that component changes. Therefore, by using the vibration sensor 2 to continuously measure the vibration generated by each component over a long period of time and observing the change in frequency over time for each component (the amount of deviation from the natural frequency), it is possible to estimate the component in which an abnormality has occurred and the time when the component will fail.

Figures 7 and 8 show examples of the frequency of each part, obtained by analyzing the vibration waveform measured by the vibration sensor 2 with an FFT analyzer. In each figure, the horizontal axis is frequency, which increases from left to right. The vertical axis is the number of measurements (total number of measurements taken for 2 seconds each time), which increases from top to bottom. This figure shows an excerpt of a specified range for both frequency and number of measurements.

Figure 7 shows the frequency generated by a part in its initial state immediately after replacement, and Figure 8 shows the frequency generated by the same part a specified time after replacement. It can be seen that the frequency generated by the part after a specified time has elapsed since replacement (Figure 8) varies from the frequency generated by the same part in its initial state (Figure 7).

Figure 9 is a graph that shows the concept of the part replacement prediction system according to this embodiment. The vertical axis of the graph represents the amount of frequency fluctuation generated by the part as the degree of anomaly, and the horizontal axis represents time.

As the components that make up the drive system of the lifting device 50 deteriorate over time, they undergo changes such as an increase in the amount of frequency fluctuation (variation from the natural frequency) generated by the component. Therefore, the relationship between the amount of frequency fluctuation generated by each component and the degree of deterioration (degree of abnormality) of the component is learned in advance, and a threshold value is set for the amount of frequency fluctuation based on the learned model.

When the amount of change in the frequency of a certain part exceeds the threshold, a warning email like that shown in Table 1 is automatically sent from the higher-level device (e.g., WCS500) that received the measurement value of the vibration sensor 2 to the terminal device of the operator of the logistics center via an information and communication network such as the Internet.

また、振動センサ２の計測値を受信した上位デバイスは、図１０に示すような深層学習等の人工知能（ＡＩ）技術を用い、部品の周波数の変動量（入力値）と、前述した学習済みモデルに基づいて設定した閾値との比較を行って部品の交換時期を予測し（出力値）、上記警告メールと共に物流センターの運営者に交換時期を通知する。そして、当該警告メール及び交換時期の通知を受信した物流センターの運営者は、保守作業員に対し、交換対象となる部品の交換を指示する。

Furthermore, the upper device that receives the measurement value of the vibration sensor 2 uses artificial intelligence (AI) technology such as deep learning as shown in Fig. 10 to compare the amount of fluctuation in the frequency of the part (input value) with a threshold value set based on the learned model described above to predict the time to replace the part (output value), and notifies the operator of the logistics center of the replacement time together with the warning email. The operator of the logistics center, having received the warning email and the notification of the replacement time, then instructs a maintenance worker to replace the part to be replaced.

Figure 11 shows an example of applying the part replacement prediction system according to this embodiment to detecting loose bolts.

The bolts (56a, 56b, 58) that secure the drive system of the lifting device 50 to the stage 57 and frame 59 gradually loosen as the drive system vibrates, and this causes an increase in the amount of frequency fluctuation generated from the fastening portion between the bolts and the drive system. Therefore, the relationship between the amount of frequency fluctuation generated from the fastening portion and the degree of bolt loosening (degree of abnormality) is learned in advance, and a threshold value is set for the amount of frequency fluctuation based on this knowledge.

In this case too, the higher-level device that receives the measurement value of vibration sensor 2 uses artificial intelligence (AI) technology such as deep learning to compare the amount of fluctuation in the bolt frequency (input value) with a threshold value set based on the trained model described above to predict when to replace the bolt (output value) and notify the operator of the logistics center when it is time to retighten the bolts. The operator of the logistics center who receives this notification then instructs a maintenance worker to retighten the bolts that need to be retightened.

In this way, the part replacement prediction system according to this embodiment utilizes artificial intelligence (AI) technology and IoT technology to detect abnormalities before breakdowns occur, and perform maintenance before an accident occurs, making it possible to realize "predictive maintenance" = "non-stop logistics." This also makes it possible to reduce total costs.

In the above embodiment, an example was described in which the present invention was applied to a parts replacement prediction system for the lifting device 50, one of the material handling devices installed in the automated warehouse 1. However, the present invention can also be applied to a parts replacement prediction system for material handling devices other than the lifting device 50 (for example, a stacker crane type transport device 30).

According to the present invention, the entire transport system of an automated warehouse can be inspected while in operation, and accidents can be prevented by quickly addressing areas of material handling equipment where failures are expected to occur.

Therefore, according to the present invention, by utilizing artificial intelligence (AI) technology and IoT technology to detect abnormalities before breakdowns occur and to carry out maintenance before accidents occur, it is possible to realize "predictive maintenance" = "uninterrupted logistics." This also makes it possible to reduce total costs.

Therefore, the present invention can be widely used in locations where transportation systems are used in logistics centers, and is expected to make a significant contribution to future logistics management systems.

1: Automated warehouse 2: Vibration sensor 10: Shelf 20: Passage 30: Conveyor device 31: Arm 40: Storage case (storage means)
41: Second identifier 50: Lifting device 51: Lower pulley 52: Upper pulley 53: Belt 54: Motor with reducer 55: Stand 56a, 56b: Bolt 57: Stage 58: Bolt 59: Frame 60: Buffer conveyor (buffer means)
70: Product item 71: First identifier 80: Shipping case (shipping medium)
90: Picking means 91: Shipping conveyor 100: Storage warehouse section (warehouse means)
200: Picking station 300: Packing means 400: Warehouse management system (WMS)
500: Warehouse Control System (WCS)
600: Storage conveyor

Claims (6)

A part replacement prediction system that predicts the replacement time of a part by using an artificial intelligence technology using an output from a sensor installed in a part constituting a material handling device,
The sensor is a vibration sensor that detects vibration of the material handling equipment,
The vibration sensor analyzes the vibration waveform generated from the multiple components constituting the material handling equipment using an FFT analyzer to decompose the vibration waveform into frequency components of the components constituting the material handling equipment,
The artificial intelligence technology predicts the replacement time of the part by comparing a threshold value related to the amount of fluctuation of the frequency components set based on a trained model that has previously trained the relationship between the amount of fluctuation of the frequency components of the part over time and the degree of abnormality of the part with the amount of fluctuation of the frequency components of each part.
This is a parts replacement prediction system for material handling equipment. The material handling equipment part replacement prediction system according to claim 1, characterized in that the material handling equipment is a lifting device installed in a storage warehouse section. The material handling equipment part replacement prediction system according to claim 1 or 2, characterized in that the vibration sensor is attached to a part that constitutes the drive system of the material handling equipment. The part replacement prediction system for material handling equipment according to claim 3, characterized in that the drive system includes active rotation means, passive rotation means, and fixing means for fixing them. The material handling equipment part replacement prediction system according to any one of claims 1 to 4, characterized in that the sensor is retrofitted to an existing material handling equipment that does not have the sensor installed. A step of detecting vibrations generated from a component constituting a material handling device using a vibration sensor installed in the component;
A step of decomposing the vibration waveform into frequency components by analyzing the vibration waveform by an FFT analyzer;
a step of predicting the replacement time of the parts constituting the material handling equipment using an artificial intelligence technology for predicting the replacement time of the parts by comparing a threshold value related to the amount of fluctuation of the frequency components set based on a trained model that has previously trained the relationship between the amount of fluctuation of the frequency components over time and the degree of abnormality of the parts with the amount of fluctuation of the frequency components of each of the parts;
A method for predicting part replacement in material handling equipment.

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