JP2012166943A - Abnormality detection system for industrial vehicle and abnormality detection method for industrial vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an abnormality detection system for industrial vehicle and an abnormality detection method for industrial vehicle capable of precisely detecting the presence or the absence of abnormality on the basis of parameters regarding the operation state.SOLUTION: The abnormality detection system (100) for industrial vehicle obtains a plurality of parameters regarding the operation state of an industrial vehicle, compares the parameters with parameters of other industrial vehicles stored in a database (114) and detects the presence or the absence of the abnormality. The abnormality detection system (100) includes: a standard characteristic function calculation part (112) for calculating a standard characteristic function in which the parameters are let to be variable, in particular, by performing the multiple regression analysis of the parameters stored in the database (114); and an abnormality detection part (113) for detecting the presence or the absence of the abnormality on the basis of whether a deviation between the gained parameters and the standard characteristic function is a prescribed value or more.

Description

  The present invention acquires a plurality of parameters related to the operating state of an industrial vehicle performing a specific operation, and compares it with the parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle stored in the database in advance. The present invention relates to a technical field of an industrial vehicle abnormality detection system and an industrial vehicle abnormality detection method for detecting the presence or absence of abnormality in the industrial vehicle.

  For products such as industrial vehicles that perform specific operations, it is possible to quickly detect an abnormality that occurs when a defect occurs and perform repair work, or to detect an abnormality and take preventive measures prior to the occurrence of the defect. It is important for maintenance management. The causes of abnormalities in products vary widely depending on the characteristics of the products, and it is not easy to detect abnormalities quickly and accurately. In particular, in products such as industrial vehicles, the number of components is very large, so efficient detection of abnormalities is desired.

  Such an abnormality detection in a specific product has been conventionally performed by inspecting components by visual inspection by an operator. For example, in Patent Document 1, a peephole is provided to devise the structure so that the amount of wear in a drum brake mounted on a vehicle can be visually detected. However, since the number of places that can be inspected is limited by visual inspection by an operator, it is easy to cause an abnormality detection failure. In addition, because it depends exclusively on the inspection work of the workers, it is impossible to detect the abnormality at all in the absence of the worker, the detection efficiency of the abnormality is poor, and it is difficult to provide a high-quality maintenance service. .

  In view of such circumstances, in recent years, development of a system for detecting an abnormality by automatically measuring a parameter relating to an operating state of a product in actual operation and analyzing the measurement result has been advanced. For example, in Patent Document 2, an integrated value is calculated for a clutch mounted on a vehicle by obtaining energy absorbed in the clutch, and the clutch life or the like is determined depending on whether or not the integrated value has reached a life limit value. A system is disclosed that can determine the presence or absence of an abnormality by determining the remaining clutch life. Patent Document 3 discloses a system that performs abnormality determination by automatically calculating the amount of wear in a vehicle tire.

  Further, in Patent Document 4, whether or not there is an abnormality in a product, equipment, etc. at the time of inspection carried out after actual operation, the measurement data obtained at the time of inspection is stored in a different place from the storage destination of the measurement data An information management device that can be determined by comparing with shipping data is disclosed. According to this, unlike the reference data shown in the specification, it is possible to detect the presence / absence of an abnormality with high accuracy based on authentic shipping data relating to the product or the like.

Japanese Utility Model Publication No. 6-35689 JP 2010-65795 A JP 2005-349966 A JP 2004-232343 A

  In Patent Document 2 and Patent Document 3, the presence / absence of an abnormality is detected based on a predetermined threshold value of the measurement value. This threshold value is defined in advance based on, for example, the design value or specification value of the product, but the actual product varies depending on factors such as individual differences. For this reason, it may not be possible to accurately detect the presence or absence of an abnormality based on a preset threshold value.

  Further, in Patent Document 4, abnormality detection is performed based on the shipping data so as to cope with such factors as individual differences. However, recording the shipping data for all products is very cumbersome in terms of work, and there is a problem that the burden required for constructing a data recording system increases.

  The present invention has been made in view of the above problems, and provides an industrial vehicle abnormality detection system and an industrial vehicle abnormality detection method capable of accurately detecting the presence or absence of an abnormality based on parameters relating to the operating state of the industrial vehicle. For the purpose.

  In order to solve the above problems, an abnormality detection system for an industrial vehicle according to the present invention acquires a plurality of parameters related to the operating state of an industrial vehicle performing a specific operation, and a plurality of parameters other than the industrial vehicle stored in advance in a database. In the industrial vehicle abnormality detection system for detecting the presence or absence of abnormality in the industrial vehicle by comparing with the parameter acquired in the past from the industrial vehicle, the parameter stored in the database is subjected to multiple regression analysis. An abnormality in the industrial vehicle based on whether a deviation between the parameter acquired with respect to the industrial vehicle and the standard characteristic function is a predetermined value or more And an abnormality detection unit for detecting the presence or absence of the above.

  According to the present invention, an abnormality detection in a specific industrial vehicle is calculated based on parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle, and the standard characteristic function and the anomaly detection target industry are calculated. This is done by comparing parameters obtained from the vehicle. The standard characteristic function statistically represents the standard behavior in other industrial vehicles of the parameter by multiple regression analysis. Therefore, it is possible to detect anomalies with high accuracy based on statistical data by determining whether or not the deviation from the standard characteristic function is a predetermined value or more.

  Preferably, the standard characteristic function is a second parameter that is another parameter included in the plurality of parameters when the first parameter that is one parameter stored in the database reaches a predetermined reference value. It is good to calculate as a function which uses as a variable the 1st variable computed as above, and the 2nd variable which consists of at least one parameter contained in a plurality of above-mentioned parameters.

  Further, the system further comprises a type classification unit that classifies the tendency of the usage mode of the industrial vehicle into any of a plurality of predetermined types based on the acquired parameters, and the standard characteristic function calculation unit is included in the database. Of the stored parameters, the standard characteristic function may be calculated based on a parameter for an industrial vehicle classified into the same type as the industrial vehicle. In this case, since it can be compared with the characteristics of other industrial vehicles having the same usage pattern, it is possible to more accurately evaluate the presence / absence of abnormality in the industrial vehicle to be detected.

  As an aspect of the present invention, for example, the first parameter is a maintenance cost per unit period of the industrial vehicle, the second parameter is an operating time of the industrial vehicle, and the second variable is the industry It may be the working time of the vehicle. The maintenance cost per unit period with the passage of operating time includes information on abnormalities in the industrial vehicle, such as the degree of deterioration of industrial vehicles and the likelihood of malfunctions. By calculating a standard characteristic function for the maintenance cost and comparing it with the acquired parameters, it is possible to accurately evaluate the presence or absence of an abnormality in the industrial vehicle subject to abnormality detection.

  As another aspect of the present invention, the industrial vehicle includes a traveling unit for traveling and a working unit for performing work other than traveling, and the first parameter is driving of the working unit. It is a parameter related to the state. In this case, it is possible to detect whether or not there is an abnormality in the working unit among the industrial vehicles having the traveling unit and the working unit.

  In this aspect, in particular, the industrial vehicle is a forklift vehicle including a lift arm and a driving mechanism for performing a cargo handling operation that is a cargo lifting / lowering operation as the working unit, and the first parameter is the lift parameter This is the displacement amount acquired from the displacement sensor for detecting the displacement amount with respect to the reference position of the arm. In this case, in a forklift vehicle that is a kind of industrial vehicle, it is possible to detect the presence or absence of an abnormality in the lift arm that is the working unit based on the displacement amount acquired from the displacement sensor.

  In addition, the industrial vehicle is a forklift vehicle including a lift arm for performing a cargo handling operation that is a lifting and lowering operation of cargo as the working unit and a driving mechanism thereof, and the first parameter is the lift arm and the ground It may be a contact force acquired from a load cell for detecting the contact load. In this case, based on the contact force acquired from the load cell instead of the displacement sensor, it is possible to detect whether there is an abnormality in the lift arm of the forklift vehicle that is a type of industrial vehicle.

The industrial vehicle includes a drum brake mechanism provided in a drive wheel as the traveling unit, and the first parameter is for detecting a displacement amount between brake shoes constituting the drum brake mechanism. The displacement amount acquired from the displacement sensor may be used.
In this case, the presence or absence of abnormality of the traveling part of the forklift vehicle, which is a kind of industrial vehicle, particularly the drum brake mechanism provided in the drive wheel of the traveling part, is detected based on the displacement amount acquired from the displacement sensor. Can do.

  In order to solve the above-described problem, an abnormality detection method for an industrial vehicle according to the present invention acquires a plurality of parameters related to an operating state of an industrial vehicle performing a specific operation, and a plurality of parameters other than the industrial vehicle stored in advance in a database. In the industrial vehicle abnormality detection method for detecting the presence or absence of abnormality in the industrial vehicle by comparing with the parameter acquired in the past from the industrial vehicle, the parameter stored in the database is subjected to multiple regression analysis. A standard characteristic function calculating step for calculating a standard characteristic function having a variable as a variable, and whether or not a deviation between a parameter acquired for the industrial vehicle and the standard characteristic function is a predetermined value or more, an abnormality in the industrial vehicle And an abnormality detection step for detecting the presence or absence of the above.

  According to the industrial vehicle abnormality detection method of the present invention, the above-described industrial vehicle abnormality detection system (including the above-described various aspects) can be suitably realized.

  According to the present invention, an abnormality detection in a specific industrial vehicle is calculated based on parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle, and the standard characteristic function and the anomaly detection target industry are calculated. This is done by comparing parameters obtained from the vehicle. The standard characteristic function statistically represents the standard behavior in other industrial vehicles of the parameter by multiple regression analysis. Therefore, it is possible to detect anomalies with high accuracy based on statistical data by determining whether or not the deviation from the standard characteristic function is a predetermined value or more.

It is a conceptual diagram which shows the utilization environment of the operation management apparatus for industrial vehicles which concerns on this invention. It is a flowchart which shows the operation processing content of the vehicle-mounted computer in 1st Example. It is a flowchart which shows the operation processing content of the server in 1st Example. It is an example of the classification | category reference | standard in a 1st type classification process. FIG. 5 conceptually shows the classification criteria shown in FIG. 4 on a graph. It is an example of the classification reference | standard in a 2nd type classification process. FIG. 7 conceptually shows the classification criteria shown in FIG. 6 on a graph. It is a graph which shows transition with respect to the working time of the maintenance cost per unit period in a certain forklift. It is a graph which shows the relationship between time T1 when the maintenance cost per unit period arrives at the threshold value C1 designated beforehand, and the cargo handling work time of a forklift. It is a schematic diagram which shows the attachment position of the displacement sensor in the lift drive part of a forklift. It is an enlarged view which expands further and shows the attachment position of the displacement sensor shown in FIG. It is a graph which shows the typical transition of the displacement amount X of the lift arm of a forklift. It is a graph which shows the relationship between the production load I1 in which the displacement amount X of a lift arm reaches | attains the threshold value X1 designated beforehand, and the cargo handling work time of a forklift. It is a graph which shows the typical transition with respect to the integrated load I of the load load X to a lift arm. It is a schematic diagram which shows the structure of the drum brake provided in the wheel of the driving wheel of a forklift. It is a graph which shows the typical transition with respect to the travel distance L of the displacement amount X of the piston with respect to a wheel cylinder. It is a graph which shows the relationship between the travel distance L1 in which the displacement amount X of the piston with respect to a wheel cylinder reaches the threshold value X1 designated beforehand, and the operating time of a forklift.

  Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. However, the dimensions, materials, shapes, relative arrangements, and the like of the components described in this embodiment are not intended to limit the scope of the present invention unless otherwise specified, but are merely illustrative examples. Not too much.

  FIG. 1 is a conceptual diagram showing a use environment of an industrial vehicle abnormality detection system 100 (hereinafter referred to as “system 100”) according to the present invention. The system 100 is a system for detecting an abnormality in a forklift 200 used at a work site such as a warehouse-type retail store such as a factory, a warehouse, or a home center, a cargo station, or a port. The forklift 200 is an example of an industrial vehicle that performs a specific operation in the present invention, and includes a traveling unit for traveling and a working unit for performing a cargo handling operation as an operation other than traveling. The forklift 200 is provided with a communication antenna 201 for communicating with the relay device 150 installed at the work site. By accessing the communication line 170 via the relay device 150, the forklift 200 is connected to the base where the server 110 is installed. Various types of information can be transmitted and received. The server 110 also includes a control unit 111 for supervising operation processing of the server 110, a standard characteristic function calculation unit 112 for calculating a standard characteristic function, and an abnormality detection for detecting an abnormality based on the calculated standard characteristic function. Unit 113, a database 114 for storing and storing data used for various processes of the system 100, and a communication unit 115 for accessing the communication line 170. Detailed functions and operations of these units will be sequentially described later. I will do it.

  The forklift 200 is equipped with an in-vehicle computer 130 for supervising various controls of the forklift 200. The in-vehicle computer 130 is provided inside the forklift 200, and an operating time measuring means for measuring the operating time of the forklift 200, a travel distance measuring means for measuring the travel distance of the forklift 200, and the forklift 200 for handling work. Work time measuring means for measuring the operated work handling time is provided, and various measurement data are measured and acquired. The acquired various data are configured to be transmitted from the communication antenna 201 to the server 110 side as well as used for various controls in the in-vehicle computer 130. The measured operating time, travel distance, and cargo handling work time are examples of parameters relating to the operating state of the forklift 200 that is an industrial vehicle, and are stored in the database 114 by being transmitted to the server 110.

  The communication line 170 may be a computer network such as the Internet, a core network of a communication carrier, and various local networks.

  The in-vehicle computer 130 also detects the forklift 200 for a predetermined inspection item based on an inspection program stored in advance in a built-in memory (not shown) at a timing when the operation time measured by the operation time measuring means reaches a predetermined value. Execute the periodic inspection mode to perform the inspection. Here, the operation content of the periodic inspection mode is defined in advance in the inspection program. If necessary, the contents of the inspection program may be variably set according to the operating state of the forklift 200 (or a predetermined program corresponding to the operating state is selected from a plurality of inspection programs).

(First embodiment)
In the first embodiment, the in-vehicle computer 130 is provided with an input unit that can be operated by an operator. Here, the input unit is various input devices such as a keyboard and a mouse. The operator of the in-vehicle computer 130 inputs a maintenance cost for the forklift 200 on which the in-vehicle computer 130 is mounted by operating the input unit. The maintenance cost is input by an operator operating an input unit provided in the in-vehicle computer 130 regularly or irregularly.

  In this way, the information regarding the maintenance cost acquired by the in-vehicle computer 130 is used for various controls by the in-vehicle computer 130 in the same manner as the above-described various measurement data (measurement data such as operating time, travel distance, cargo handling work time, etc.) Transmission is also possible from the communication antenna 201 to the server 110 side. The acquired information regarding the maintenance cost is also an example of information regarding the operating state of the forklift 200 which is an industrial vehicle.

  Here, with reference to FIG. 2, the contents of the operation processing of the in-vehicle computer 130 will be described. FIG. 2 is a flowchart showing the operation processing contents of the in-vehicle computer 130 in the first embodiment.

  First, the in-vehicle computer 130 monitors and acquires the operating time measured by the operating time measuring means at regular or irregular timing (step S101). Then, when the execution time of the periodic inspection mode specified in advance by the program is reached, the periodic inspection mode is executed to perform the periodic inspection (step S102). Thereby, the in-vehicle computer 130 collects the measurement data for the predetermined inspection items defined in the inspection program, including the above-described various measurement data (measurement data such as operation time, travel distance, and cargo handling work time) ( Step S103). Then, each inspection data is compared with a corresponding threshold defined in the inspection program, and a periodic inspection is performed by making a pass / fail judgment for each inspection item.

  Subsequently, the in-vehicle computer 130 calculates and acquires the total amount of maintenance costs appropriately input by the operator from the input unit after the previous periodic inspection is performed (step S104). The maintenance cost input operation by the operator is appropriately performed when the maintenance cost is generated, and is stored in the memory of the in-vehicle computer 130. In step S104, calculation and acquisition are performed by the in-vehicle computer 130 accessing the memory.

  Subsequently, the in-vehicle computer 130 transmits these various measurement data to the server 110 side via the communication antenna 201 (step S105), and ends the processing (END). Information transmitted to the server 110 at this time includes, in addition to the various measurement data described above, identification information of the forklift 200 (information for identifying an individual such as a model, a model number, a serial number, an arrangement location), Information about past operation history may be included. As a result, the server 110 can identify the individual from a large number of forklifts 200 deployed in various locations based on the received information and manage the operating state.

  Next, with reference to FIG. 3, operation processing on the server 110 side that has received transmission of various data from the in-vehicle computer 130 will be described. FIG. 3 is a flowchart showing the operation processing contents of the server 110 in the first embodiment.

  When the control unit 111 of the server 110 receives various data transmitted from the in-vehicle computer 130, the control unit 111 stores the received various data in the database 114 (step S201). Thereafter, the control unit 111 accesses the database 114 to classify the operating state of the forklift 200 into a predetermined type based on the stored various data (steps S202 and S203). Particularly in the present embodiment, the type of operating state is performed in two stages over the first type classification process and the second type classification process.

First, in the first type classification process, a classification process based on the maintenance cost and the operation rate per unit period calculated based on the received various data is performed (step S202). Here, the maintenance cost and the operation rate per unit period may be calculated by the following equations.
Maintenance cost per unit period = total maintenance cost / elapsed time (1)
Occupancy rate = operation time / elapsed time (2)

  FIG. 4 is an example of classification criteria in the first type classification processing, and FIG. 5 conceptually shows the classification criteria shown in FIG. 4 on a graph. There are four types that can be classified in the first type classification process: “relocation type”, “waste car type”, “continuous holding type”, and “replacement type”.

  First, the “relocation type” is defined as a type in which the maintenance cost per unit period is 0 to 500 (¥ / year) and the operation rate is 0 to 50 (%). In short, the “relocation type” has a low maintenance cost because the aging of the forklift 200 is not progressing, but the utilization rate is low and it is not effectively used. It is a type.

  The “waste car type” is defined as a type in which the maintenance cost per unit period is 500 to (¥ / year) and the operation rate is 0 to 50 (%). In short, the “waste car type” is a vehicle that is obsolete and has high maintenance costs, and has a low operation rate and is wasted. It is a type.

  The “continuous holding type” is defined as a type in which the maintenance cost per unit period is 0 to 500 (¥ / year) and the operation rate is 50 to (%). In short, the “continuous holding type” has a high operation rate and is effectively used, and the maintenance cost is low because it has not deteriorated. Therefore, maintaining the current status is a type that is rational in terms of management.

  The “replacement type” is defined as a type in which the maintenance cost per unit period is 500 to (¥ / year) and the operation rate is 50 to (%). In short, the “replacement type” is a type that has a high operating rate and is frequently used, but is aging and maintenance costs are high, so it is reasonably manageable to replace it with a new vehicle.

  Next, in the second type classification process, a classification process based on the travel distance and the cargo handling work time included in the received various data is performed (step S203).

  Here, FIG. 6 is an example of the classification standard in the second type classification process, and FIG. 7 conceptually shows the classification standard shown in FIG. 6 on the graph. There are four types, “low operation type”, “loading type”, “traveling type”, and “high operation type”, which can be classified in the second type classification process.

  First, the “low operation type” is defined as a type in which a cargo handling work time is 0 to 300 (hour / year) and a travel distance is 0 to 5000 (km / year). In short, the “low operation type” is a type in which the frequency of performing cargo handling work and the frequency of traveling are low. The “loading type” is defined as a type in which a cargo handling work time is 300 to (hour / year) and a travel distance is 0 to 5000 (km / year). In short, the “loading type” is a type in which the cargo handling work is performed more frequently than the traveling frequency. The “traveling type” is defined as a type in which a cargo handling work time is 0 to 300 (hour / year) and a traveling distance is 5000 to (km / year). In short, the “traveling type” is a type that travels more frequently than the frequency at which cargo handling work is performed. The “high operation type” is defined as a type in which a cargo handling work time is 300 to (hour / year) and a travel distance is 5000 to (km / year). In short, the “high operation type” is a type that has a high frequency of performing cargo handling work and traveling.

  Returning to FIG. 3 again, the standard characteristic function calculation unit 112 of the server 110 accesses the database 114 to calculate the standard characteristic corresponding to the classification in steps S202 and S203 (step S204).

  Here, with reference to FIG. 8 and FIG. 9, the specific content of the standard characteristic calculated in step S204 will be described. FIG. 8 is a graph showing the transition of the maintenance cost per unit period for an individual forklift 200 with respect to the operating time. As the operation time of the forklift 200 increases, the maintenance cost per unit period tends to increase due to deterioration of each part or occurrence of a malfunction. Here, T1 is an operation time during which the maintenance cost per unit period reaches a predetermined threshold value C1.

  Next, FIG. 9 is a graph showing the relationship between the time T1 when the maintenance cost per unit period reaches a predetermined threshold value C1 and the cargo handling work time of the forklift 200. Generally, as the cargo handling work time increases, the degree of deterioration of the forklift 200 or the probability of occurrence of a failure increases, and the time T1 decreases.

  Here, since the actual forklift 200 has individual differences and differences in the environment in which it is used, the above tendency (the tendency that the time T1 becomes shorter as the cargo handling work time increases) is shown by the black data points in FIG. As shown in FIG. The standard characteristic function calculation unit 112 of the server 110 is a data distribution having such variance based on data acquired from other past forklifts (typically the same model or the same type of forklifts) stored in the database 114. Is obtained, and an approximate line is calculated by performing multiple regression analysis on the data distribution using a least square method or the like, and a standard characteristic is calculated.

  In the present embodiment, among the data stored in the database 114, the standard characteristic function calculation unit 112 uses the data related to the forklift 200 of the individual having the same type as the types classified in steps S202 and S203 to It is preferable to calculate the characteristics. Thereby, since it can be compared with the characteristic in the forklift 200 which has the same usage form, the presence or absence of abnormality in the forklift 200 to be managed can be accurately evaluated as described below.

  Returning to FIG. 3 again, the server 110 calculates the time T1 in the forklift 200 to be managed based on the acquired various data (step S205). Then, the abnormality detection unit 113 of the server 110 evaluates whether there is an abnormality in the forklift 200 to be managed based on the time T1 calculated in step S205 and the cargo handling work time included in the various data received in step S201. (Steps S206 to S209).

  Here, it is assumed that the data point based on the time T1 calculated in step S205 and the cargo handling work time included in the various data acquired in step S201 is at the position indicated by the outlined data point in FIG. Here, the cargo handling work time received in step S201 is T2, and the difference from the standard characteristic at time T2 is calculated as ΔT1 (step S206).

  Then, the abnormality detection unit 113 determines whether or not the calculated ΔT1 is greater than a predetermined threshold value K (step S207). As a result, when ΔT1 is larger than the threshold value K (step S207: YES), it is determined that there is an abnormality in the forklift 200 to be managed (step S208). On the other hand, when ΔT1 is equal to or less than the threshold value K (step S207: NO), it is determined that there is no abnormality in the forklift 200 to be managed (step S209).

  As described above, according to the system 100 of the present invention, the abnormality detection in the forklift 200 as an industrial vehicle is calculated based on the parameters acquired in the past from other forklifts, and the standard characteristic function and the abnormality are detected. This can be done by comparing parameters acquired from the industrial vehicle to be detected. The standard characteristic function statistically represents the standard behavior in other industrial vehicles of the parameter by multiple regression analysis. Therefore, it is possible to detect anomalies with high accuracy based on statistical data by determining whether or not the deviation from the standard characteristic function is a predetermined value or more.

(Second embodiment)
The first embodiment described above is characterized in that the presence or absence of abnormality in the forklift 200 to be managed is evaluated by paying attention to the maintenance cost per unit period as a parameter. In this case, it is possible to determine whether there is an abnormality, but there remains a problem that it is impossible to determine which part of the forklift 200 has an abnormality. This problem can be solved in the second embodiment described below. In the following description, parts common to the first embodiment are denoted by common reference numerals, and unless otherwise specified, the configuration, function, and action are the same. It will be omitted as appropriate.

  FIG. 10 is a schematic diagram illustrating a configuration of a forklift 200 that is a detection target of the system 100 according to the second embodiment. In the second embodiment, a displacement sensor 220 is provided in the lift arm 230 as a specific part of the forklift 200 that performs abnormality determination, and the presence or absence of abnormality in the lift driving unit 210 is evaluated based on measurement information from the displacement sensor 220. Can do. FIG. 11 is an enlarged view showing the attachment position of the displacement sensor 220 shown in FIG.

  In the present embodiment, an example in which the presence or absence of abnormality in the lift drive unit 210 is evaluated as a specific part of the forklift 200 will be described. However, the present invention can be similarly applied to other parts of the forklift 200.

  The forklift 200 includes a lift arm 230 for supporting cargo from below when performing a cargo handling operation, and a lift drive unit 210 for driving the lift arm 230 in the vertical direction. Here, the lift drive unit 210 includes a hydraulic cylinder mechanism (not shown) as a power source, and is configured to be able to drive and control the lift arm 230 in the vertical direction in accordance with a control signal from the vehicle body side. .

  The displacement sensor 220 is installed at the lowermost part (bottom part) of the lift arm 230, measures the distance from the lowermost part of the lift arm 230 to the ground facing the lowermost part, and outputs the measurement result as an electric signal. To the in-vehicle computer 130. In particular, in this embodiment, when the periodic inspection mode is executed by the in-vehicle computer 130, the lift arm 230 is set to the lowest position (standard position) by the lift drive unit 210, and in this state, the lift sensor 230 is moved by the displacement sensor 220. The distance between the lowermost part and the ground facing the lowermost part (hereinafter referred to as “the displacement amount of the lift arm 230”) is measured.

  FIG. 12 is a graph showing a typical transition of the displacement amount X of the lift arm 230 of the forklift 200. In FIG. 12, the horizontal axis represents the integral amount I of the operating time and the loaded weight, and the vertical axis represents the displacement amount X of the lift arm 230. The forklift 200 has a lift arm that is deformed by the deflection (distortion) of the lift arm 230 due to the weight of the load and wear and deformation of each component around the lift arm 230 including the lift drive unit 210 as the operation time or the cargo handling work time elapses. The displacement amount X of 230 changes.

  Specifically, as the operating time of the forklift 200 increases, deflection due to the weight of the lift arm 230 and changes in component parts due to aging increase, so the displacement amount X of the lift arm 230 decreases (ie, the lift arm Deflection increases as 230 approaches the ground side). Such a decrease amount of the displacement amount X increases as the weight of the load loaded during the handling operation increases. Therefore, as shown in FIG. 12, the displacement amount X of the lift arm 230 has a tendency to gradually decrease as the integral amount I of the operating time and the loaded weight increases. Here, an integrated load at which the displacement amount X of the lift arm 230 reaches a predetermined threshold value X1 is defined as I1.

  FIG. 13 is a graph showing the relationship between the production load I1 at which the displacement amount X of the lift arm 230 reaches the predetermined threshold value X1 and the cargo handling work time of the forklift 200. Generally, the displacement amount X of the lift arm 230 also decreases as the ratio of the cargo handling work time to the operation time of the forklift 200 increases. Therefore, the accumulated load I1 becomes shorter as the cargo handling work time increases.

  Here, since the actual forklift 200 has individual differences and differences in the environment in which it is used, the above tendency (the accumulated load I1 tends to decrease as the cargo handling work time increases) is the same as the period T1 in FIG. Have dispersion. The standard characteristic function calculation unit 112 of the server 110 acquires a data distribution having such a variance based on past data stored in the database 114, and performs a multiple regression analysis such as a least square method on the data distribution. By calculating the approximate line, the standard characteristic function is calculated. That is, in this embodiment, instead of the maintenance cost per unit period in the first embodiment, the same processing is performed on the displacement amount X of the lift arm 230, thereby calculating the standard characteristics related to the displacement amount X of the lift arm 230. To do.

  Based on the standard characteristic regarding the displacement amount X of the lift arm 230 calculated in this way, the abnormality detection unit 113 calculates and compares the integrated load I1 in the forklift to be managed, thereby driving the lift drive of the forklift 200 to be managed. The presence or absence of an abnormality in unit 210 can be evaluated.

  Here, the displacement sensor 220 is used to evaluate whether there is an abnormality in the lift arm 230, but a load cell that detects the contact force between the lift arm 230 and the ground may be used instead. Similar to the displacement amount X of the lift arm 230, the contact force X between the lift arm 230 and the ground has a predetermined behavior with respect to the integrated load I of the operating time and the loaded weight. Specifically, as shown in FIG. 14, the contact force X between the lift arm 230 and the ground increases as the integrated load I increases. Also in this case, the presence or absence of abnormality in the lift drive unit 210 can be evaluated by defining the integrated load I1 at which the load load X reaches a predetermined threshold and performing the same analysis as in the case of the displacement sensor.

(Third embodiment)
The second embodiment described above is characterized in that the forklift 200 is evaluated for the presence or absence of an abnormality in the lift arm 230. In the third embodiment, as another part of the forklift 200, an example in which the presence or absence of abnormality of the drum brake 300 provided in the wheel of the driving wheel of the forklift 200 can be detected will be described. In the following description, the parts common to the first and second embodiments are denoted by the same reference numerals, and the configuration, function, and action are the same unless otherwise specified. Detailed description will be omitted as appropriate.

  FIG. 15 is a schematic diagram showing a configuration of a drum brake 300 provided in the wheel of the driving wheel of the forklift 200. The drum brake 300 includes a drive unit 310 including an extendable piston 311 and a wheel cylinder 312, a pair of brake shoes 330 that drives the anchor unit 320 so as to spread outward as the drive unit 310 operates, and an adjusting A bar 340, a parking lever 350, and a parking cable 360 are provided. In particular, a displacement sensor 370 for detecting the amount of movement of the piston is attached to the inner wall of the wheel cylinder 312 by a fixing member 380 in the drive unit 310 including the piston 311 that can be expanded and contracted and the wheel cylinder 312.

  Here, the braking method by the drum brake 300 will be described. First, when the driver depresses the brake pedal (not shown), the hydraulic pressure generated in the master cylinder (not shown) is applied to the drive unit 310. Then, in the drive unit 310, the piston 311 moves outward (see the arrow direction in FIG. 15) with a predetermined amount of displacement with respect to the wheel cylinder 312, and the pair of brake shoes 330 pressed by the piston 311 are driven wheels. A braking force is generated by being pressed against the inside of the foil. Here, the predetermined displacement amount X of the piston 311 with respect to the wheel cylinder 312 tends to decrease as wear of the brake shoe 330 progresses. Specifically, as shown in FIG. 16, the displacement amount X increases as the travel distance increases. Here, a travel distance at which the displacement amount X reaches a predetermined threshold value X1 is defined as L1.

  FIG. 17 is a graph showing the relationship between the travel distance L1 at which the displacement amount X of the piston 311 with respect to the wheel cylinder 312 reaches a predetermined threshold value X1 and the operating time of the forklift 200. Here, since the actual forklift 200 has individual differences and differences in the environment in which it is used, the tendency of the L1 has a variance similar to the period T1 in FIG. The standard characteristic function calculation unit 112 of the server 110 acquires a data distribution having such a variance based on past data stored in the database 114, and performs a multiple regression analysis such as a least square method on the data distribution. By calculating the approximate line, the standard characteristic function is calculated. That is, in this embodiment, instead of the maintenance cost per unit period in the first embodiment, the same processing is performed on the displacement amount X of the piston 311 with respect to the wheel cylinder 312 to calculate the standard characteristic related to the displacement amount X. To do.

  By calculating and comparing L1 in the management-target forklift 200 based on the standard characteristic function relating to the displacement calculated in this way, it is possible to evaluate whether there is an abnormality in the drum brake 300 of the management-target forklift 200. .

  The present invention acquires a plurality of parameters related to the operating state of an industrial vehicle performing a specific operation, and compares it with the parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle stored in the database in advance. The present invention is applicable to an industrial vehicle abnormality detection system and an industrial vehicle abnormality detection method for detecting the presence or absence of an abnormality in the industrial vehicle.

DESCRIPTION OF SYMBOLS 100 Industrial vehicle abnormality detection system 110 Server 111 Control part 112 Standard characteristic function calculation part 113 Abnormality detection part 114 Database 115 Communication part 130 Car-mounted computer 150 Relay apparatus 170 Communication line 200 Forklift 201 Communication antenna 220 Displacement sensor 230 Lift arm 300 Drum brake 310 Driving unit 320 Anchor unit 330 Brake shoe

Claims (9)

By acquiring a plurality of parameters related to the operating state of an industrial vehicle that performs a specific operation and comparing it with the parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle stored in the database in advance, In the abnormality detection system for industrial vehicles that detects the presence or absence of abnormality,
A standard characteristic function calculation unit that calculates a standard characteristic function using the parameter as a variable by performing multiple regression analysis of the parameter stored in the database;
An abnormality detection unit that detects whether there is an abnormality in the industrial vehicle based on whether or not a deviation between the parameter acquired for the industrial vehicle and the standard characteristic function is a predetermined value or more. Anomaly detection system for industrial vehicles.   The standard characteristic function is calculated as a second parameter that is another parameter included in the plurality of parameters when a first parameter that is one parameter stored in the database reaches a predetermined reference value. 2. The abnormality detection for an industrial vehicle according to claim 1, wherein the first variable is calculated as a function having a first variable and a second variable including at least one parameter included in the plurality of parameters. system. Further comprising a type classification unit for classifying the tendency of the use mode of the industrial vehicle into any one of a plurality of predetermined types based on the acquired parameters;
The said standard characteristic function calculation part calculates the said standard characteristic function based on the parameter about the industrial vehicle classified into the same type as the said industrial vehicle among the parameters stored in the said database. The abnormality detection system for industrial vehicles according to 1 or 2.   The first parameter is a maintenance cost per unit period of the industrial vehicle, the second parameter is an operating time of the industrial vehicle, and the second variable is a working time of the industrial vehicle. The abnormality detection system for an industrial vehicle according to any one of claims 1 to 3, wherein the system is an abnormality detection system for an industrial vehicle. The industrial vehicle has a traveling unit for traveling and a working unit for performing work other than traveling,
The industrial vehicle abnormality detection system according to any one of claims 1 to 3, wherein the first parameter is a parameter related to a driving state of the working unit. The industrial vehicle is a forklift vehicle including a lift arm and a drive mechanism for performing a cargo handling operation that is a cargo lifting operation as the working unit,
6. The industrial vehicle abnormality detection system according to claim 5, wherein the first parameter is a displacement amount acquired from a displacement sensor for detecting a displacement amount with respect to a reference position of the lift arm. The industrial vehicle is a forklift vehicle including a lift arm and a drive mechanism for performing a cargo handling operation that is a cargo lifting operation as the working unit,
6. The industrial vehicle abnormality detection system according to claim 5, wherein the first parameter is a contact force acquired from a load cell for detecting a contact load between the lift arm and the ground. The industrial vehicle includes a drum brake mechanism provided in a drive wheel as the traveling unit,
6. The industrial vehicle abnormality detection according to claim 5, wherein the first parameter is a displacement amount acquired from a displacement sensor for detecting a displacement amount between brake shoes constituting the drum brake mechanism. system. By acquiring a plurality of parameters related to the operating state of an industrial vehicle that performs a specific operation and comparing it with the parameters acquired in the past from a plurality of industrial vehicles other than the industrial vehicle stored in the database in advance, In the abnormality detection method for industrial vehicles that detects the presence or absence of abnormality,
A standard characteristic function calculating step of calculating a standard characteristic function using the parameter as a variable by performing multiple regression analysis of the parameter stored in the database;
An abnormality detection step of detecting presence or absence of an abnormality in the industrial vehicle based on whether or not a deviation between the parameter acquired for the industrial vehicle and the standard characteristic function is a predetermined value or more. Anomaly detection method for industrial vehicles.