Vehicle body management system

Abstract

The present application provides a vehicle body management system which manages a vehicle body having a drive system composed of a plurality of components including an engine, the vehicle body management system comprising: a processing device which calculates an efficiency value of a monitoring object based on information of the vehicle body detected by a sensor provided to the vehicle body, the monitoring object being the drive system, a component or a subsystem of the drive system; and an output terminal which outputs the efficiency value of the monitoring object calculated by the processing device, the processing device calculating a load parameter of the drive system, determining whether the load parameter is greater than a load determination value set in advance, calculating the efficiency value of the monitoring object based on an input energy and an output energy of the monitoring object with the condition that the load parameter is greater than the load determination value, and recording the calculated efficiency value of the monitoring object.

Description

Technical Field

[0001] This invention relates to a vehicle body management system for managing a vehicle body having a transmission system consisting of multiple components including an engine. Background Technology

[0002] Known technologies include driving skill improvement techniques based on collecting and analyzing vehicle body data (Patent Document 1) and damage prevention technologies to prevent vehicle body damage (Patent Document 2). Patent Document 1 discloses a technique that calculates fuel consumption (fuel consumption / distance) for the same driving range and uses the range fuel consumption normalized to the load weight as a benchmark to evaluate the driver's driving skills. Patent Document 2 discloses a technique that prevents vehicle body damage by limiting the load weight to ensure that the effective cumulative gradient is within a specified range.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: JP Patent No. 6329904

[0006] Patent Document 2: JP Patent No. 2011-501264 Summary of the Invention

[0007] However, Patent Documents 1 and 2 assume that the vehicle body is in a normal state. Therefore, there are concerns that appropriate results may not be obtained if the engine, generator, AC-DC converter, inverter, motor, or other components of the transmission system malfunction. Transmission system malfunctions are a major cause of increased CO2 emissions or fuel consumption. From the perspective of mitigating environmental and fuel cost impacts, accurately identifying transmission system malfunctions is crucial. While it's possible to determine transmission system malfunctions based on range-bound fuel consumption, range-bound fuel consumption fluctuates significantly depending on the driver's skill and driving environment, regardless of whether the transmission system is malfunctioning. Therefore, it's difficult to reasonably determine transmission system malfunctions based solely on range-bound fuel consumption.

[0008] The purpose of this invention is to provide a vehicle body management system that can reasonably determine abnormalities in the transmission system.

[0009] To achieve the above objectives, the present invention provides a vehicle body management system for managing a vehicle body having a transmission system comprising multiple components including an engine. The vehicle body management system includes: a processing unit that calculates the efficiency of a monitored object based on information detected by sensors located on the vehicle body, the monitored object being the transmission system, a component of the transmission system, or a subsystem thereof; and an output terminal that outputs the efficiency of the monitored object calculated by the processing unit. The processing unit calculates load parameters of the transmission system, determines whether the load parameters are greater than a pre-set load judgment value, calculates the efficiency value of the monitored object based on the input energy and output energy of the monitored object, and records the calculated efficiency value of the monitored object, provided that the load parameters are greater than the load judgment value.

[0010] Invention Effects

[0011] According to the present invention, abnormalities in the transmission system can be reasonably determined. Attached Figure Description

[0012] Figure 1 The diagram shows a dump truck (dump truck) as an example of a vehicle body that is managed by the vehicle body management system of the first embodiment of the present invention.

[0013] Figure 2 yes Figure 1 The diagram shows a schematic of the transmission system of a dump truck.

[0014] Figure 3 This is a schematic diagram of the vehicle body management system according to the first embodiment of the present invention.

[0015] Figure 4 This is a graph showing an example of the calculated results of the engine's output efficiency value.

[0016] Figure 5 This is a diagram showing an example of the calculated efficiency values ​​of a subsystem consisting of a generator and a rectifier.

[0017] Figure 6 This is a graph showing an example of the calculated efficiency value of an inverter.

[0018] Figure 7 This is a diagram showing an example of the calculated efficiency value of an electric motor used for driving.

[0019] Figure 8 This is a diagram illustrating an example of data acquired while a dump truck is in motion.

[0020] Figure 9 This represents the value of DC current relative to the fuel injection quantity from... Figure 8The data was extracted and plotted as a result of data obtained under the condition that the load capacity was greater than the specified value.

[0021] Figure 10 This indicates the use of the attached location and time to... Figure 9 The graph shows the results obtained by distinguishing the data from each point and calculating the average value of each point relative to the DC value of the fuel injection quantity.

[0022] Figure 11 It means from Figure 10 The data shown is a graph of the results from which the base data for the average calculation was extracted and the number of data points exceeded a specified value.

[0023] Figure 12 It means to Figure 11 A graph showing the relationship between fuel injection quantity and efficiency value (KPI).

[0024] Figure 13 This is an example of a box plot showing the efficiency values ​​for a transmission system under normal conditions.

[0025] Figure 14 This is an example of a box plot showing efficiency values ​​when an anomaly occurs in the transmission system.

[0026] Figure 15 This is a flowchart illustrating an example of a calculation procedure for the efficiency value of a monitored object of a processing device included in a vehicle body management system according to the first embodiment of the present invention.

[0027] Figure 16 This is a flowchart illustrating an example of a procedure for selecting the efficiency value of a monitoring object of a processing device included in a vehicle body management system according to the first embodiment of the present invention.

[0028] Figure 17 This is a flowchart illustrating an example of a procedure for determining anomalies or signs of an object monitored by a processing device included in the vehicle body management system according to the first embodiment of the present invention.

[0029] Figure 18 This is an example of a display screen showing a report in an output terminal.

[0030] Figure 19 This is an example of a display screen showing a report in an output terminal.

[0031] Figure 20 This is a diagram illustrating an example of the average fuel injection amount for each grid cell in a position coordinate system.

[0032] Figure 21 It means from Figure 20The data extracted is a graph of examples of data where the number of sampled data points per grid unit is greater than a specified value.

[0033] Figure 22 It means from Figure 21 The data extracted includes a graph of examples of fuel injection quantities that are greater than the specified value.

[0034] Figure 23 This is a flowchart illustrating an example of a calculation procedure for the efficiency value of a monitored object of a processing device included in a vehicle body management system according to the second embodiment of the present invention. Detailed Implementation

[0035] Hereinafter, embodiments of the present invention will be described using the accompanying drawings.

[0036] (First Embodiment)

[0037] -Body-

[0038] Figure 1 The diagram shows an example of a dump truck as the vehicle body managed by the vehicle body management system according to the first embodiment of the present invention. The dump truck 1 shown in the diagram has a chassis 2 and a plurality of wheels rotatably mounted on the chassis 2. The wheels include left and right front wheels 3f and left and right rear wheels 3r. One front wheel 3f is disposed at each of the left and right ends of the front portion of the chassis 2. Two rear wheels 3r are disposed at each of the left and right ends of the rear portion of the chassis 2. The front wheels 3f are steering wheels that are steered according to the steering angle input via a steering wheel, etc., and are driven wheels that are driven by the road surface of the driving path of the dump truck 1. Electric motors 15 for driving the left and right rear wheels 3r are connected to the rotation shaft of the rear wheels 3r, which are the drive wheels. Figure 2 ), and a speed reducer 19 that adjusts the rotational speed of the left and right rear wheels 3r. Figure 2 ).

[0039] In addition, the dump truck 1 includes a deck 4, a cab 5, a control housing 6, and multiple mesh boxes 7. The deck 4 serves as the operator's walkway and is positioned above the front wheels 3f. The cab 5 is the operator's cab, located on the upper surface of the deck 4. Besides the operator's seat, the cab 5 also includes control pedals (accelerator pedal, brake pedal, etc.) for controlling the dump truck 1's speed, and the aforementioned steering wheel. The control housing 6 houses various electrical equipment and is located at the front of the vehicle. The mesh boxes 7 are for housing the drivetrain 10 of the dump truck 1. Figure 2 The device that radiates the residual energy of the regenerative power generated during braking as heat is located at the rear of the control housing 6.

[0040] The dump truck 1 also includes a cargo box 8 and a hydraulic lifting cylinder 9. The cargo box 8 is a platform for loading sand, ore, or other cargo, and is connected to the chassis frame 2 via a hinge pin 8p, allowing it to undulate relative to the chassis frame 2. The hydraulic lifting cylinder 9, located in front of the hinge pin 8p, connects the chassis frame 2 and the cargo box 8, causing the cargo box 8 to undulate by extending and retracting. Figure 1 In the middle, the engine 11 is located in the part hidden by the front wheel 3f of the dump truck 1. Figure 2 ) or generator 12 ( Figure 2 ), etc., and above these machines is a vehicle-mounted controller 30 ( Figure 3 ).

[0041] In addition, the dump truck 1 is equipped with a device for connecting to the output terminal 51 ( Figure 3 ) or a server located at a distance 40 ( Figure 3 C1 is a communication device for sending and receiving data between devices such as ) and others.

[0042] -Transmission System-

[0043] Figure 2 yes Figure 1 The diagram shows a schematic of the transmission system of a dump truck. In the same diagram, regarding the transmission system of a dump truck... Figure 1 The element labeling already explained in the text and Figure 1 The same reference numerals are used in the accompanying drawings, and the descriptions are omitted.

[0044] Figure 2 The transmission system 10 shown is a system that transmits the power of the engine to the drive wheels, and is configured to include an engine 11, a generator 12, a rectifier 13, left and right inverters 14, and left and right electric motors 15 for driving.

[0045] Engine 11 is an engine (internal combustion engine) that burns the injected fuel, converting the heat of the fuel into mechanical engine output (rotational power) to drive generator 12, cooling fan 16, and hydraulic pump (not shown). When the operator operates the control pedals located in the cab 5, the engine 11 injects a fuel injection amount corresponding to the pedal operation amount, and the engine 11 rotates at a rotational speed corresponding to the fuel injection amount.

[0046] Furthermore, the aforementioned cooling fan 16 is a device that generates cooling air to cool the engine 11, etc., and is mechanically connected to the engine 11 via a clutch. The hydraulic pump is the hydraulic source for the hydraulic actuator of the hydraulic lift cylinder 9, etc., and is mechanically connected to the engine 11.

[0047] The generator 12 is connected to the mechanical engine 11 and is driven by the engine 11 to convert the engine output into three-phase alternating current. The electricity generated by the generator 12 mainly serves as the power source for the left and right electric motors 15 used for driving.

[0048] The rectifier 13 is an AC-DC converter that converts the AC power generated by the generator 12 into DC power. It is housed together with the capacitor 17, chopper 18, and the left and right inverters 14 within the control housing 6. The rectifier 13 and capacitor 17 form a modular, water-cooled structure that rectifies and smooths the three-phase AC power from the generator 12 to convert it into DC power. The chopper 18 is located between the capacitor 17 and the inverters 14 to obtain the regenerative power generated by the inverters 14.

[0049] The left and right inverters 14 are configured as IGBTs (Insulated Gate Bipolar Transistors) with high insulation withstand voltage using semiconductor elements, controlling the operation of the left and right driving electric motors 15. The left inverter 14 is connected to the left driving electric motor 15, and the right inverter 14 is connected to the right driving electric motor 15. When the accelerator pedal is depressed, these left and right inverters 14 convert the DC power from the rectifier 13 into three-phase AC power corresponding to the request, and when the brake pedal is depressed, they rectify the power generated by the left and right driving electric motors 15.

[0050] The left and right driving electric motors 15 are three-phase induction motors. When the accelerator pedal is depressed, they are driven by three-phase AC power from the inverter 14, which converts the three-phase AC power into mechanical motor output (rotational power). The rotational power of the left and right driving electric motors 15 is transmitted to the rear wheels 3r via the reducer 19, thereby propelling the vehicle. When the brake pedal is depressed, the driving electric motors 15 generate electricity. The remaining electricity generated by the driving electric motors 15 is either accumulated in the capacitor 17 via the inverter 14 or discharged.

[0051] In addition, the dump truck 1 is equipped with sensors S1 to S7 ( Figure 3 Sensors such as S1 to S7 and position measuring device S8 (same as above) are used. Signals from sensors S1 to S7 and position measuring device S8 are output to the vehicle controller 30. Figure 3 ).

[0052] Sensor S1 is a fuel injection quantity sensor that detects the amount of fuel injected into the engine 11. This sensor S1 can be a potentiometer that uses, for example, the amount of operation of the aforementioned operating pedal as a value corresponding to the fuel injection quantity.

[0053] Sensor S2 is an engine output sensor that detects the output of engine 11. This sensor S2 can be a rotational speed sensor that detects the rotational speed of engine 11, or a torque sensor that detects the torque of engine 11, which are values ​​corresponding to the engine output. The rotational speed sensor or torque sensor can be located on the output shaft of engine 11.

[0054] Sensor S3 is a DC sensor that detects DC power generated by generator 12 and converted to DC power by rectifier 13, and sensor S4 is an AC sensor that detects AC power output from inverter 14. These sensors can be, for example, electricity meters.

[0055] Sensor S5 is a motor output sensor that detects the output of the electric motor 15 for driving. This sensor S5 can be a rotational speed sensor that detects, for example, the rotational speed of the electric motor 15 for driving, as a value corresponding to the motor output, or a torque sensor that detects the torque of the electric motor 15 for driving. The rotational speed sensor or torque sensor can be located on the output shaft of the electric motor 15 for driving.

[0056] Sensor S6 is a vehicle tilt angle sensor that measures the tilt angle of the dump truck 1 relative to a reference plane (e.g., a horizontal plane), and an acceleration sensor (IMU, etc.) can be used, for example. Sensor S7 is a load capacity sensor that measures the load, that is, the weight of the cargo loaded in the truck bed 8, and a strain gauge can be used, for example. The load capacity can be calculated from, for example, the amount of deformation of the chassis 2 caused by the weight of the cargo, or from the load applied to the axles of the front wheels 3f and the rear wheels 3r.

[0057] Position determination device S8, for example, is a GNSS receiver, which will receive data from an artificial satellite ST ( Figure 3 The received antenna position data is output to the vehicle controller 30. In the vehicle controller 30, the position of the reference point (e.g., the center of gravity of the vehicle body) of the dump truck 1 in the Earth coordinate system (or a separately defined coordinate system) is calculated from the antenna position and the known body size data of the dump truck 1.

[0058] -Vehicle Management System-

[0059] Figure 3 This is a schematic diagram of a vehicle body management system according to a first embodiment of the present invention. The vehicle body management system shown in the diagram is a system for managing unloading trucks 1a, 1b, etc., and is configured to include a processing unit 20 and at least one (two are shown in the same figure) output terminal 51, 52. Two dump trucks 1a and 1b are shown in the same figure, but the vehicle body management system can also use three or more dump trucks. Dump trucks 1a and 1b respectively correspond to... Figure 1 as well as Figure 2 The dump truck 1 described below. After the processing device 20 is described below, the output terminals 51 and 52 are described in the "-output terminal-" column.

[0060] -Processing Unit-

[0061] The processing device 20 is configured to include at least one computer with the function of calculating the efficiency value of a monitored object (described later), wherein the monitored object is the transmission system 10, components or subsystems of the transmission system 10 of the dump trucks 1a and 1b. In the processing device 20, the efficiency value is calculated based on messages (data) detected by sensors (e.g., sensors S1 to S7) installed on the dump trucks 1a and 1b. The processing device 20 in this embodiment is configured to include an onboard controller 30 and a server 40. The onboard controller 30 and the server 40 will be described later in the "-Onboard Controller-" and "-Server-" columns respectively.

[0062] Furthermore, "monitored object" refers to the respective transmission system 10, its individual components, or subsystems that are being unloaded. For example, by selecting a specific monitored object using the output terminal, the efficiency value data of the selected monitored object is downloaded from the processing device to the output terminal, allowing the output terminal to confirm the efficiency value data. In this case, the entire transmission system 10, a single component, or a subsystem can be selected as the monitored object. Additionally, the selection of the monitored object for confirming the efficiency value can be changed sequentially. It is also possible to select multiple monitored objects simultaneously.

[0063] "Components" refers to the various devices that make up the transmission system (primarily devices that convert energy). Figure 2 In the example described, engine 11, generator 12, rectifier 13, inverter 14, and electric motor 15 for driving correspond to the components.

[0064] A "subsystem" refers to a system smaller than the unit transmission system, comprising multiple components that make up part of the transmission system. Figure 2 In the example, when the engine 11, generator 12, rectifier 13, inverter 14, and driving electric motor 15 are considered as a series of components, examples include "engine 11 + generator 12" and "engine 11 + generator + rectifier 13" as subsystems. Besides this, as subsystems including generator 12, "generator 12 + rectifier 13", "generator 12 + rectifier 13 + inverter 14", and "generator 12 + rectifier 13 + inverter 14 + driving electric motor 15" can also be used. Furthermore, "rectifier 13 + inverter 14", "rectifier 13 + inverter 14 + driving electric motor 15", and "inverter 14 + driving electric motor 15" also correspond to subsystems respectively.

[0065] "Efficiency value" refers to the ratio of output energy to input energy of a monitored object, calculated based on both input and output energy. For example, the efficiency value can be calculated by dividing the output energy by the input energy (or by percentage). A higher efficiency value means that input energy is converted into output energy at a higher rate within that monitored object. The efficiency value decreases relatively under certain abnormal conditions affecting the monitored object.

[0066] -Vehicle Controller-

[0067] The on-board controller 30 is a computer mounted on dump trucks 1a and 1b, and its main function is to collect data such as efficiency values ​​of the monitored objects of the dump trucks equipped with the on-board controller 30. Here, the on-board controller 30 of dump truck 1a has been described, but the on-board controllers of other dump trucks have the same configuration.

[0068] The vehicle controller 30 has an input interface 31, an RTC (real-time clock) 32, a memory 33, a CPU 34, and an output interface 35.

[0069] Signals from sensors S1 to S7 or position measuring device S8, etc., mounted on the dump truck, are input to the input interface 31. Various signals from the sensors are digitally converted as needed, and the CPU 34 is used as the input / output power source to process these signals. The signals are then stored in the memory 33 as needed.

[0070] RTC32 is the clock hand of the vehicle controller 30. RTC32 marks time in units of 0.1 seconds, for example, and the time marked by RTC32 is appended to the aforementioned input / output energy, etc., as time data. In addition, in the vehicle controller 30, for values ​​calculated by the CPU 34 based on input / output energy (e.g., efficiency values ​​of each monitored object), the time data of the input / output energy, which forms the basis of the calculation, is also inherited. It can be configured to append the calculated time to the calculated value.

[0071] The memory 33 is a storage device having a storage area for storing programs executed by the CPU 34, values ​​calculated or selected by the CPU 34, and the time of each value. Although not shown, the memory 33 includes a ROM or HDD for storing programs and various values ​​for executing operations based on the CPU 34, and RAM, which is the working area when the CPU 34 executes the program.

[0072] CPU 34 has the following functions: during, for example, when supplying power to the vehicle controller 30, or during the movement of the dump truck, it performs various processes (calculations of the input / output energy or efficiency values ​​of the monitored object, various judgments, etc.) according to the program read from memory 33. Figure 15 as well as Figure 16 ).

[0073] Output interface 35 is a device for outputting data to other vehicle-mounted machines such as the aforementioned communication device C1 or a monitor (not shown) in the driver's cab 5. Data output from output interface 35 to communication device C1 is transmitted to server 40 via communication device C1, wireless communication line (radio wave) WL, the nearest repeater RP, and network NT. Repeater RP is, for example, a wireless LAN access point, router, or base station. Network NT is, for example, the Internet. In addition, communication device C1 can also directly send and receive data with other machines such as output terminal 51 via wireless medium BT without going through network NT. Wireless medium BT is, for example, infrared or radio waves.

[0074] -server-

[0075] Server 40 is a computer capable of statistically analyzing data on the efficiency values ​​of each monitored object, such as dump trucks 1a, 1b, etc., or determining anomalies in each monitored object. Server 40 is, for example, located in a management center. The management center may be a facility operated by a manufacturer of, for example, dump truck 1a, but it can also be a facility operated by a business partner that handles management services from manufacturers, users, or dealers of dump trucks 1a and 1b. Server 40 includes input / output interfaces 41, memory 42, and a CPU 43, etc.

[0076] The input / output interface 41 is a device that functions in relation to the communication device C1 of the dump truck, transmitting and receiving data with the communication device C2 or the output terminal 52 via a network such as wired communication, wireless communication, or LAN. Efficiency values ​​received by the communication device C2 from the dump trucks 1a, 1b, etc., via the network NT are input to the server 40 through the input / output interface 41 and recorded (uploaded to) the memory 42. For example, when accessing the server 40 from the output terminal 51, the specified data recorded in the memory 42 is downloaded to the output terminal 51 via the input / output interface 41, the communication device C2, the network NT, the repeater RP, and the wireless communication line WL.

[0077] The memory 42 is a storage device having a storage area for storing programs executed by the CPU 43, data input to the server 40, data processed by the CPU 43, etc. Although not shown, the memory 42 includes a ROM or HDD for storing programs for performing operations based on the CPU 43 and various values, and RAM that serves as the working area when the CPU 43 executes the program.

[0078] CPU 43 performs the following functions: Based on a predefined program stored in memory 42, it statistically analyzes the efficiency data collected by the vehicle controller 30, and determines whether the monitored object is normal or abnormal based on the statistical data. Figure 17 ).

[0079] -Output Terminal-

[0080] Output terminals 51 and 52 are terminals used to display or output efficiency values, statistical data, and judgment results of the monitored objects calculated by the processing unit 20, and for confirmation by, for example, the driver, manager, or service personnel of a dump truck. Output terminal 51 is a portable terminal with a display, such as a smartphone, tablet PC, or laptop PC. Output terminal 52 is an example of a fixed terminal such as a printer or desktop PC. Although not shown, the output terminals can also be installed inside the cab 5 of the dump truck, allowing the driver or others to confirm the efficiency values, statistical data, and judgment results of the monitored objects calculated by the processing unit 20. The monitor of the server 40 is also a type of output terminal.

[0081] Output terminal 51 can access server 40 via the nearest repeater RP and network NT. Since output terminal 51 is a mobile terminal, it can access server 40 without selecting a specific location, as long as the wireless communication line (radio wave) WL of the nearest repeater RP can be received. Furthermore, in cases such as confirming data from dump truck 1a, as long as the distance between output terminal 51 and dump truck 1a is within the reach of the wireless medium BT, it is possible to access vehicle controller 30 from output terminal 51 without using network NT. According to output terminal 51, data downloaded from server 40 or vehicle controller 30 can be viewed on its display using a pre-installed application. The data is displayed on the display in a pre-defined report format. In this case, vehicle controller 30 and server 40 can be configured to display data in report format for efficiency values, etc., and output terminal 51 can be used to view reports or data from a specified period for a given dump truck. Additionally, output terminal 51 can be configured to convert data downloaded from vehicle controller 30 and server 40 into report format and display it using an application.

[0082] Output terminal 52 can access server 40 via a closed-loop network such as LAN within a management center where server 40 is located, and can display or print data downloaded from server 40. Similar to output terminal 51, it can also display and print data in a prescribed report format on a monitor. Alternatively, output terminal 52 can be configured to convert data downloaded from server 40 into report format and display or print it using an application.

[0083] Furthermore, although examples are given from in Figure 3 The output terminal 52 set up in the management center can access the server 40 via a closed-loop network such as a LAN, but it can also be configured to allow access to the server 40 from other fixed terminals that can be connected to the network NT.

[0084] -Methods for selecting efficiency values-

[0085] If a component in the transmission system malfunctions, its energy loss will increase, leading to a decrease in efficiency. Therefore, by calculating and monitoring the efficiency value of the monitored object, the malfunction can be determined. Figure 2 In one example, by calculating the ratio of any two values ​​among the fuel injection quantity of engine 11, engine output, DC output of rectifier 13, inverter output, and motor output, the efficiency value of the monitored object, which sets these values ​​as input and output energy, can be calculated. The on-board controller 30 can calculate the fuel injection quantity from the signal of sensor S1, the engine output from the signal of sensor S2, the DC output from the signal of sensor S3, the inverter output from the signal of sensor S4, and the motor output from the signal of sensor S5.

[0086] For example, the efficiency value of engine 11 can be calculated based on the engine output (output energy) calculated from the signal from sensor S2 and the fuel injection quantity (input energy) calculated from the signal from sensor S1. Figure 4 This example shows the calculated result of the engine output efficiency value. Similarly, the efficiency value of generator 12 (component), or generator 12 and rectifier 13 (subsystem), can be obtained from the signals of sensors S2 and S3. Figure 5 An example of the calculation results for the efficiency value of a subsystem consisting of generator 12 and rectifier 13 is shown. The efficiency value of inverter 14 is obtained from the signals of sensors S3 and S4, and the efficiency value of electric motor 15 for driving is obtained from the signals of sensors S4 and S5. Figure 6 An example of the calculated efficiency value of inverter 14 is shown. Figure 7 An example of the calculated efficiency value of the electric motor 15 for driving is shown.

[0087] In addition, the efficiency values ​​of the subsystem consisting of engine 11, generator 12, and rectifier 13 are calculated from the signals of sensors S1 and S3, and the efficiency value of the subsystem with added inverter 14 is calculated from the signals of sensors S1 and S4. The efficiency values ​​of the subsystem consisting of generator 12, rectifier 13, and inverter 14 are calculated from the signals of sensors S2 and S4, and the efficiency value of the subsystem with added driving electric motor 15 is calculated from the signals of sensors S2 and S5. The efficiency values ​​of the subsystem consisting of inverter 14 and driving electric motor are calculated from the signals of sensors S3 and S5. The overall efficiency value of the transmission system is calculated from the signals of sensors S1 and S5.

[0088] like Figures 4-7As shown, the efficiency value of a normal monitored object is roughly proportional to the input energy and output energy. However, in the case of, for example, engine 11, the calculated efficiency value is affected by factors such as the engine 11's rotational speed, boost pressure, exhaust temperature, and cooling temperature, resulting in deviations. Furthermore, in the case of the generator 12 and rectifier 13 subsystem, it is affected by factors such as the dump truck's travel speed, cooling pump, travel motor blower, inverter blower, power generation efficiency, and conversion efficiency. For inverter 14, it is affected by DC-DC conversion (chopper) or inverter temperature; for electric motor 15, it is affected by travel resistance (road slope or acceleration) or motor temperature. Therefore, even if the instantaneous efficiency value of the monitored object is calculated from the input and output energy at a certain moment without considering the scenario, it cannot be said that the calculated efficiency value is a suitable value for evaluating the condition of the monitored object.

[0089] Figure 8 This is an example of data acquired while the dump truck is in motion. In the same graph, the upper section shows vehicle speed, load capacity (weight of cargo loaded in the truck bed), fuel injection quantity, DC power (rectifier output), and motor output. When considering... Figure 2 The transmission system 10 described in the text has been obtained Figure 8 When using data, if based on Figure 8 By obtaining the ratio of fuel injection quantity (input energy) to motor output (output energy), the overall efficiency value of the transmission system 10 can be calculated. For example, if the ratio of input to output energy is obtained by using the value of fuel injection quantity to DC current, the efficiency value of the engine 11, which is a subsystem or component consisting of generator 12 and rectifier 13, can be calculated. If the ratio of input to output energy is obtained by using DC current and motor output, the efficiency value of the subsystem consisting of inverter 14 and electric motor 15 can be calculated. Of course, the efficiency value of each component (e.g., engine 11) can also be calculated by detecting the necessary data, but in this way, the efficiency value can be calculated even for a subsystem unit that combines multiple components.

[0090] As will be discussed later, when using the efficiency value of a monitored object to determine whether it is normal or abnormal, the efficiency value used for judgment based on data calculations during driving under high load conditions is particularly important for improving the accuracy of the judgment results. High load conditions are defined as conditions where the energy input to the engine exceeds a specified value. Engine 11 employs an engine... Figure 2In this example, typically, the fuel injection quantity becomes a load parameter, and a high load condition can be set when the fuel injection quantity is above a predetermined position. In this embodiment, the fuel injection quantity can be calculated from the operation amount of the operating pedal (signal of sensor S1), and when an automatic speed adjustment device is provided, it can also be calculated from the engine control signal based on the automatic speed adjustment device. In the case of a dump truck that uses an electric motor for the engine 11, the power supplied to the electric motor (cart power, etc.) can be set as a load parameter.

[0091] In addition, there is a tendency for increased fuel injection or electrical supply when driving on an incline. From this perspective, the incline of the driving surface can also be considered as one of the load parameters defining high-load conditions, and an incline exceeding a predetermined load threshold can be defined as a high-load condition. The incline of the driving surface can be calculated from the signal of sensor S6. Acceleration resistance can also be used as a load parameter defining high-load conditions. Furthermore, in Figure 8 In this context, if we focus on load capacity, we can observe a general increase in fuel injection quantity when the load capacity is much larger compared to a very small load capacity. This trend also allows us to consider load capacity as one of the load parameters defining high load conditions; a load capacity exceeding a specified load threshold can also be considered a high load condition.

[0092] Figure 9 It refers to the value of DC current relative to the fuel injection quantity from Figure 8 The data extracted was obtained under the condition that the load capacity was greater than the specified value. The subsystem consisting of engine 11, generator 12, and rectifier 13 (in relation to...) Figures 9-12 In the following description, the efficiency values ​​of the subsystem are expressed in terms of DC electric current / fuel injection quantity, therefore, it is possible to target Figure 9 The efficiency values ​​of each data processing subsystem. However, the data extracted solely using the load capacity as a filter is as follows: Figure 9 As shown, residual deviations exist due to various major factors such as driving conditions, making it difficult to utilize [the data / methods]. Figure 9 The efficiency value calculated from the data determines whether the subsystem is normal or abnormal.

[0093] Figure 10 To utilize the attached location and time... Figure 9 The result is obtained by distinguishing the various data points and calculating the average value of each distinction for the DC value relative to the fuel injection quantity. Figure 10The points depicted represent the average DC current relative to the average fuel injection quantity, calculated based on data from the same time and driving areas. The driving area is a coordinate system divided by a grid (e.g., 30m × 30m), allowing data to be distinguished by which grid the accompanying position data belongs to. The time area is divided at predetermined intervals, allowing data to be distinguished by which time area (time interval) the data was acquired. For comparison... Figure 9 and Figure 10 Therefore, although relative to Figure 9 exist Figure 10 While the deviation of the efficiency value data is reduced, it is difficult to determine with high precision whether the subsystem is normal or abnormal.

[0094] Figure 11 From Figure 10 The data shown extracts results from a subset of data used in the average calculation, where the amount of data is above a specified threshold. In other words, from... Figure 10 The result of removing the average number of data points below the specified value is Figure 11 .exist Figure 11 This approach effectively suppresses data bias. In examples like this, where data is roughly selected based on high-load conditions related to load capacity using the driving area and time region, and an average value is calculated each time a distinction is made to extract a large amount of basic data, bias caused by external factors such as driving environment or driving skills is suppressed.

[0095] Figure 12 It is Figure 11 This data is converted into the relationship between fuel injection quantity and efficiency value (KPI). For example, in... Figure 12 As can be confirmed, the deviation in the calculated efficiency values ​​becomes smaller in the high-load region with high fuel injection volume. This is because the variation in engine speed or boost pressure is smaller in the high-load region than in the low-load region, and the energy consumed by auxiliary equipment such as the cooling pump or motor blower is also less in the high-load region. Figure 12 In this context, regions where the efficiency value deviation is below a specified allowable value are defined as regions where the fuel injection quantity is greater than X. In this case, a load judgment value X pre-set for the fuel injection quantity is stored in memory 33, and the robust efficiency value of the high-load region where the fuel injection quantity is greater than the load judgment value X is evaluated, thereby improving the appropriateness of judging whether the monitored object is normal or abnormal.

[0096] In addition, due to Figure 12 The data was pre-screened based on load capacity. Figure 9Therefore, extracting fuel injection quantities above the load determination value X becomes a screening process related to the second high load condition. However, if an increase in the amount of data is permissible, either the load-based screening or the fuel injection quantity-based screening (e.g., the former) can be omitted, and data can be extracted using a single high load condition. Conversely, the data range can be further narrowed down using three or more high load conditions.

[0097] -Methods for determining anomalies-

[0098] Figure 13 This is an example of a box plot showing the efficiency values ​​when the transmission system is functioning normally. Figure 14 This is an example of a box plot showing efficiency values ​​when an anomaly occurs in the transmission system. In these plots, the horizontal axis represents the date, and the vertical axis represents the efficiency value. For a single dump truck under monitoring, the interquartile range and median value are calculated based on daily efficiency statistics. Figure 3 In the vehicle body management system, settings are configured to determine abnormal and normal values ​​for monitored objects based on efficiency values. For example, when the transmission system 10 is normal, statistics are collected for each monitored object within a specified period. Figure 12 Data, based on which to create within the specified period Figure 13 The data. Then, for each monitored object, based on the data, an anomaly determination value D1 for determining the occurrence of an anomaly and a precursor determination value D2 for determining the precursor of an anomaly are pre-set and stored in memory 33, 42. Figure 3 At least 42 memory units in the memory.

[0099] like Figure 13 As shown, the anomaly determination value D1 can be set as the minimum value of the first quartile (the value at the bottom of each box) of the interquartile range relative to the efficiency values ​​of each day within a specified period, by only a specified margin. Alternatively, the anomaly determination value D1 can also be set as the minimum value of the median value (the second quartile) of the efficiency values ​​of each day within a specified period, by only a specified margin. The CPU43 (or CPU34) calculates the interquartile range for the efficiency values ​​of the monitored object, as follows: Figure 14 As shown, if the median value of the efficiency value of a certain monitored object is lower than the anomaly determination value D1, it is determined that an anomaly has occurred in the monitored object.

[0100] like Figure 13 As shown, the interquartile range (interquartile range) between the maximum and minimum values ​​of the efficiency values ​​for each day within a specified period can be set to increase by a specified margin as the precursor judgment value D2. For example, the CPU43 (or CPU34) calculates the interquartile range based on the efficiency values ​​of the monitored object. Then, as... Figure 14As shown, if the difference between the maximum and minimum interquartile range of a certain monitored object, that is, if the deviation of the data exceeds the warning judgment value D2, it is judged that there is a warning of an anomaly occurring in the monitored object.

[0101] Furthermore, when comparing efficiency statistics for the same dump truck across different time periods (e.g., the current day and the previous day), if these statistics change and exceed a pre-set judgment value D3, it can be determined that an abnormality or its precursor has occurred in the transmission system. The judgment value D3 is stored in memories 33 and 42. Figure 3 At least memory 42 of the memory is used. When using the determination value D3 to determine the condition of a monitored object, statistical data of efficiency values ​​calculated for each monitored object are accumulated in at least memory 42 of the memories 33 and 42. For example, for a monitored object of the same dump truck, the difference between the statistical data of efficiency values ​​for a newly calculated specified period (e.g., one day) calculated by CPU 43 (or CPU 34) and the statistical data of efficiency values ​​for a past specified period (e.g., the previous day) stored in memory is calculated. If the magnitude of this difference is greater than the determination value D3 read from memory, it can be determined that there is an abnormality or a sign of an abnormality in the monitored object. As a statistical data for comparison, the median value can be used as an example, but the difference between the maximum and minimum values ​​of the interquartile range can also be considered. It is possible to consider setting the determination value D3 to be larger when it is determined to be an abnormality and to set the determination value D3 to be smaller when it is determined to be a sign. It is also possible to consider setting two determination values ​​D3, one large and one small, to distinguish between abnormality and sign.

[0102] Statistical data on efficiency values ​​obtained from multiple dump trucks operating within the same time zone and driving area are compared. If the difference in these statistical data is greater than a pre-set judgment value D4, it can be determined that an abnormality or its precursor has occurred in the transmission system. The judgment value D4 is stored in memory 33, 42 ( Figure 3 At least 42 units of memory are required in the memory. Figure 3Taking a scenario where dump trucks 1a and 1b are traveling in the same area and at the same location at the same time as an example, this explains how to determine if there is an abnormality or a sign of an abnormality in the transmission system of dump truck 1a. In this case, the statistical data of the efficiency value of a monitored object obtained from the travel of dump truck 1a over a specified period (e.g., one day) is compared with the statistical data of the efficiency value of a monitored object obtained from the travel of dump truck 1b in the same area and at the same location at the same time (e.g., the same day). The statistical values ​​of the efficiency values ​​of dump trucks 1a and 1b can be compared using data stored in memory 42. If the statistical data Za of the efficiency value of dump truck 1a is significantly smaller than the statistical data Zb of the efficiency value of dump truck 1b by more than the determination value D4 (Zb > Za + D4), it can be determined that there is an abnormality or a sign of an abnormality in the monitored object of dump truck 1a. As a statistical data for comparison, the median value can be used as an example, but the difference between the maximum and minimum values ​​of the interquartile range can also be considered. Similar to the judgment value D3, it is possible to set the judgment value D4 to a larger value when an anomaly is detected, and to set the judgment value D4 to a smaller value when a warning sign is detected. Alternatively, it is possible to set two judgment values ​​D4, one large and one small, to distinguish between anomalies and warning signs.

[0103] Furthermore, it is possible to compare the statistical data of dump truck 1a with the statistical data of multiple other dump trucks traveling in the same area at the same time. In this case, representative values ​​(e.g., average, median, maximum) of the statistical data of the other multiple dump trucks are calculated and compared with the statistical data of dump truck 1a.

[0104] -Efficiency value calculation and processing-

[0105] Figure 15 This is a flowchart illustrating an example of a calculation program based on the efficiency value of the monitored object of the processing device. During power supply periods or while the dump truck equipped with the onboard controller 30 is in motion, the onboard controller 30 reads the program stored in the memory 33 and repeatedly executes it using the CPU 34 at 0.1-second intervals. Figure 15 as well as Figure 16 The process. After Figure 15 The process calculates the efficiency value of each monitored object under high load conditions and records it together with the location code (described later) in memory 33.

[0106] [Step S11]

[0107] When it begins Figure 15During the process, in step S11, the vehicle controller 30 inputs signals from sensors S1 to S7 and the position measuring device S8, and the CPU 34 calculates at least one current load parameter of the transmission system 10 in real time based on the input signals. "Current" refers to the current (that is, this processing cycle). The load parameter calculated here is a value used to determine the aforementioned high load conditions, such as the fuel injection quantity of the engine 11, the load capacity of the cargo, or the road slope. The fuel injection quantity can be calculated from the signal of sensor S1, the load capacity can be calculated from the signal of sensor S7, and the road slope can be calculated from the signal of sensor S6.

[0108] [Step S12]

[0109] In the next step S12, the vehicle controller 30 uses the CPU 34 to determine whether the load parameter calculated in step S11 is greater than the specified load determination value X read from the memory 33. If the load parameter is greater than the load determination value X, the vehicle controller 30 transitions the program from step S12 to step S13. If the load parameter is less than the load determination value X, the vehicle controller 30 terminates the process. Figure 15 The current processing loop of the process is transitioned to the next processing loop.

[0110] Furthermore, if multiple load parameters are calculated in step S11, the program can be programmed such that the determination in step S12 is satisfied if all load parameters are greater than their respective load determination values ​​X. Alternatively, the program can be programmed to satisfy the determination in step S12 if a particular load parameter is greater than its load determination value X. It can also be considered that if three or more load parameters are calculated, the determination in step S12 is satisfied if more than half or a predetermined number of load parameters are greater than their respective load determination values ​​X. Such determination conditions only need to consider the calculated load of the processing device 20, etc.

[0111] [Step S13]

[0112] If the program transitions to step S13, the vehicle controller 30 acquires the ratio of input energy to output energy for each monitored object and calculates the efficiency value (e.g., by dividing the output energy by the input energy). This is mapped to a pre-defined efficiency function (a straight line or low-order curve, etc.) using the least squares method, thereby enabling efficiency calculation. The efficiency value of the monitored object is calculated based on the condition that the load parameter is greater than the load judgment value X, omitting the calculation of efficiency values ​​for periods when the load parameter is below the load judgment value X. Each input and output energy is calculated based on the signals input to the vehicle controller 30 from sensors S1 to S7 in step S11 of the current processing loop, with data from the time measured by the RTC 32 added at the time of signal input.

[0113] [Step S14]

[0114] In the next step S14, the vehicle controller 30 calculates the current position of the dump truck (the vehicle itself equipped with the vehicle controller 30) using the CPU 34 based on the data received by the position measuring device S8 input in step S11, and appends it to the efficiency value calculated in step S13. The position calculated in this step can be a value in the usual Earth coordinate system or a value in a custom-defined xy coordinate system. Alternatively, instead of calculating the current position based on the data received by the position measuring device S8, it is also possible to process images from the dump truck's onboard camera and compare them with a database of various locations (such as a video image database) to calculate the current position. It is also possible to calculate the current position based on a driving trajectory inferred from the measured driving distance of a rangefinder or steering history records.

[0115] [Step S15]

[0116] In the next step S15, the on-board controller 30 infers the grid to which the dump truck's current position belongs and determines the position code of the inferred grid. The grid's position code can be a uniquely defined position code, such as a common position code like Geohash. The grid defines a certain size driving area in the position coordinate system, for example, an area equivalent to a 30m × 30m area on the actual ground surface. The position code is data assigned to each grid, enabling grid identification, that is, identifying the driving area.

[0117] [Step S16]

[0118] In the next step S16, the vehicle controller 30 records the various efficiency values ​​calculated in step S13, along with the position codes and time data appended to these efficiency values, into the memory 33. After completing step S16, the vehicle controller 30 terminates the process. Figure 15 The system processes the current processing loop and transitions the program to the next processing loop. The vehicle controller 30 calculates the efficiency values ​​of each monitored object in this way and records them sequentially into memory 33.

[0119] -Efficiency value selection processing-

[0120] Figure 16 This is a flowchart illustrating an example of a selection procedure based on the efficiency value of a monitored object of a processing device. For example, in... Figure 15 In step S16, whenever a new efficiency value is recorded, the on-board controller 30 performs processing based on the flowchart.

[0121] [Step S21]

[0122] When it begins Figure 16During the process, the vehicle controller 30 identifies the location code associated with the latest efficiency value recorded in the memory 33, and determines whether the initial time has been registered in the memory 33 based on the identified location code. This initial time is the starting time of the time limit set for data counting in each region (grid). If the vehicle controller 30 has registered the initial time for the location code of the efficiency value, it transitions from step S21 to step S23; otherwise, it transitions from step S21 to step S22.

[0123] [Step S22]

[0124] If the program transitions to step S22, the vehicle controller 30 registers the initial time in memory 33 for the position code identified in step S21, ends the current processing loop, and transitions the program to the next processing loop. The initial time registered in step S22 can be used to determine the existence of an efficiency value for the initial time in step S21, which is appended to the current processing loop. By registering the initial time in this way, the determination of step S21 is satisfied in subsequent processing loops, even after the efficiency value of the shared position code has been reached. The next opportunity to execute step S22 occurs when the position code of the received efficiency value changes, a set time T1 has elapsed since the registered initial time, or the number of data points for the efficiency value of the current position code reaches a set number N1. The set time T1 and the set number N1 are preset values ​​recorded in memory 33.

[0125] [Step S23]

[0126] If the program transitions to step S23, the vehicle controller 30, based on the efficiency value confirmed in step S21 of the current processing loop, uses the CPU 34 to calculate the elapsed time from the initial time currently registered in the memory 33. The elapsed time can be calculated using the difference between the current time and the initial time. The current time, as mentioned here, in step S21 of the current processing loop, can be the time specified as the time attached to the confirmation of the efficiency value with the position code registered at the current initial time. Alternatively, the current time measured in real-time by the RTC 32 can also be used.

[0127] [Step S24]

[0128] In the next step S24, the vehicle controller 30 uses the CPU 34 to read the preset time T1 from the memory 33 and determines whether the elapsed time calculated in step S23 is less than or equal to the preset time T1. If the elapsed time exceeds the preset time T1, the vehicle controller 30 transitions the program from step S24 to step S28; if the elapsed time is less than or equal to the preset time T1, the program transitions the program from step S24 to step S25.

[0129] [Step S25]

[0130] If the program transitions to step S25, the vehicle controller 30 uses the CPU 34 to calculate the number of data points of efficiency values ​​for the current position code recorded after the initial time registered in the current memory 33.

[0131] [Step S26]

[0132] In the next step S26, the vehicle controller 30 uses the CPU 34 to read the preset data number N1 from the memory 33 and determines whether the data number calculated in step S25 is greater than or equal to the preset data number N1. If the data number does not meet the preset data number N1, the vehicle controller 30 terminates the process. Figure 16 The current processing loop of the process is completed and the program is transitioned to the next processing loop. If the number of data reaches the set number of data N1, the vehicle controller 30 will transition the program from step S26 to step S27.

[0133] [Step S27]

[0134] If the program transitions to step S27, the vehicle controller 30 uses the CPU 34 to calculate the average of the efficiency values ​​of N1 location codes accumulated within a set time T1 from the initial time. By using the location-differentiated efficiency values ​​of the dump truck to measure the elapsed time from the initial time, only efficiency values ​​of more than N1 set data points collected within a set time T1 for a specific area (grid) are extracted as valid data, and the average efficiency values ​​are calculated. The calculated average efficiency value, along with the location codes and time area data, is recorded in the memory 33. These data sets are sent to the server 40 sequentially or at certain time intervals. Furthermore, the data sets recorded in the memory 33, such as... Figure 3 As explained in the text, it can also be downloaded to the output terminal 51 via wireless medium BT for viewing.

[0135] In the presence of multiple dump trucks managed by server 40, each dump truck's on-board controller 30 sends a data set with the average efficiency value, location code, and time zone appended with the vehicle ID.

[0136] Furthermore, the set time T1 and set data number N1, in addition to factors such as the grid size (the length of one side of a square) and sampling period, can be set by considering standard values ​​for the slope of the road surface and the driving speed at that slope. For example, in a scenario where the sampling period is 0.1 seconds, the grid size is 30m, and the standard uphill slope is 5m / s, the set time T1 can be set to 6 seconds based on the 30m travel time, and the set data number N1 can be set to 60 based on the number of samples taken during that period. By determining the set time T1 and set data number N1 in this way, data on efficiency values ​​calculated when the dump truck slightly crosses the grid is excluded, suppressing data bias.

[0137] [Step S28]

[0138] After calculating the average efficiency value in step S27, the vehicle controller 30 transitions the program to step S28, where the CPU 34 performs initialization, clearing the initial time registered in the memory 33 for the current position code. After clearing the initial time registration, the vehicle controller 30 terminates. Figure 16 The data statistics show the efficiency values ​​of the current processing loop of the process and the transition of the program to the next processing loop, the next time zone, or the next location of code.

[0139] If the efficiency value of the same location code has been collected for a set time T1 from the initial time before the collection of the set data number N1, the CPU 34 executes step S28 to eliminate the initial time registered for the current location code (steps S24→S28). If no efficiency value of the set data number N1 or more is collected for the same location code in the same time region (from the initial time to the set time T1), the efficiency value in that time region is not considered valid data. Invalid data during this period can be accumulated in memory 33, but to avoid unnecessary overuse of memory capacity, a configuration that does not accumulate invalid data in memory 33 or a configuration that temporarily stores but appropriately rewrites it can be adopted. Therefore, in this embodiment, only data for which the set data number N1 is collected within a consecutive set time T1 for the efficiency value of the same location code whose load parameter exceeds the load determination value X is recorded as valid data in memory 33 and sent to server 40.

[0140] -Anomaly Detection and Handling-

[0141] Figure 17This is a flowchart illustrating an example of a procedure for determining anomalies or precursors of monitored objects using a processing device. For each monitored object, processing based on this flowchart is performed by server 40 at predetermined intervals (e.g., daily). In the case of multiple managed dump trucks, the above processing is performed for each monitored object for each of its respective dump trucks.

[0142] [Step S31]

[0143] If start Figure 17 In the processing, server 40 uses CPU 43 to perform statistics on the data uploaded from vehicle controller 30 within a specified period (e.g., one day) in step S31. In this example, the median value and interquartile range (IQR) of the data for each specified period (e.g., one day) of the above-mentioned average efficiency value are calculated for the monitored object to determine the presence or absence of anomalies or premonitions, and used as statistical data.

[0144] [Step S32]

[0145] In the next step S32, the server 40 uses the CPU 43 to compare the statistical data calculated in step S31 with the corresponding anomaly determination value D1 read from the memory 42 to determine whether an anomaly exists in the monitored object. In this example, the CPU 43 determines whether the median value calculated in step S31 is greater than or equal to the corresponding anomaly determination value D1. If the median value is less than the anomaly determination value D1 and an anomaly is presumed in the monitored object, the server 40 transitions the program from step S32 to step S38. If the median value is greater than or equal to the anomaly determination value D1, the server 40 transitions the program from step S32 to step S33.

[0146] [Step S33]

[0147] After transitioning the program to step S33, server 40 uses CPU 43 to compare the statistical data calculated in step S31 with the corresponding warning value D2 read from memory 42 to determine whether there are any abnormal warning signs for the monitored object. In this example, CPU 43 determines whether the interquartile range (the difference between the maximum and minimum interquartile ranges) calculated in step S31 is below the corresponding warning value D2. If the interquartile range is greater than the warning value D2 and the monitored object shows abnormal warning signs, server 40 transitions the program from step S33 to step S37. If the interquartile range is below the warning value D2, server 40 transitions the program from step S33 to step S34.

[0148] [Step S34]

[0149] After transitioning the program to step S34, server 40 uses CPU 43 to calculate the difference between the statistical data calculated in step S31 and the statistical data recorded in memory 42 for the same dump truck (self-unloading vehicle) over a specified period (e.g., the previous day). Server 40 also uses CPU 43 to determine whether the calculated difference is below the determination value D3. In this example, if the decrease in the median value after calculating the efficiency value of the latest day relative to the median value after calculating the efficiency value of the previous day exceeds the determination value D3, and it is determined that there is a sign of an anomaly in the monitored object, server 40 transitions the program from step S34 to step S37. If the difference between the latest median value and the median value of the previous day is below the determination value D3, server 40 transitions the program from step S34 to step S35.

[0150] [Step S35]

[0151] After transitioning the program to step S35, server 40 uses CPU 43 to calculate the difference between the statistical data calculated in step S31 and the statistical data calculated for other dump trucks, and determines whether the calculated difference is below the determination value D4. In this example, if the median value calculated for the same day for the corresponding monitored object of another dump truck traveling at the same location is lower than the median value calculated in step S31 by exceeding the determination value D4, and it is determined that there are signs of an anomaly in the monitored object, server 40 transitions the program to step S37. If the difference between the median values ​​of the efficiency values ​​of the two dump trucks at the same location in the same time area is below the determination value D4, server 40 transitions the program from step S35 to step S36.

[0152] [Step S36]

[0153] If none of the determinations in steps S32 to S35 indicate any abnormality or signs in the monitored object, and the program transitions to step S36, the server 40 uses the CPU 43 to record the determination result (normal determination) indicating that there is no abnormality in the monitored object to the memory 42. This determination result can be recorded in the memory 42 and simultaneously converted into report data using the CPU 43, and then notified to output terminal 51 or output terminal 52. After the program ends in step S36, the server 40 terminates. Figure 17 The current process will remain in standby mode until the start of the next process (e.g., the next day) (or the process will transition to another dump truck or monitored object). Figure 17 (The process).

[0154] [Step S37]

[0155] In one of the steps S33-S35, after the program transitions to step S37 due to the observed signs of an anomaly in the monitored object, the server 40 uses the CPU 43 to record the determination result (sign of anomaly determination) indicating that an anomaly has occurred in the monitored object to the memory 42. This determination result can be recorded in the memory 42, and simultaneously converted into report data using the CPU 43, and notified to output terminal 51 or output terminal 52. After the program ends in step S37, the server 40 terminates. Figure 17 The current process will remain in standby until the start of the next process (e.g., the next day) (or the process will transition to another dump truck or monitored object). Figure 17 (The process).

[0156] [Step S38]

[0157] After determining in step S32 that an anomaly has occurred in the monitored object and transitioning the program to step S38, server 40 uses CPU 43 to record the determination result (anomaly determination) indicating that an anomaly has occurred in the monitored object to memory 42. This determination result can be recorded in memory 42, and simultaneously converted into report data using CPU 43 and notified to output terminal 51 or output terminal 52. After the program ends in step S38, server 40 terminates. Figure 17 The current process will remain in standby until the start of the next process (e.g., the next day) (or the process will transition to another dump truck or monitored object). Figure 17 (The process).

[0158] -The report shows-

[0159] Figure 18 as well as Figure 19 This is an example diagram showing the display of a report in the output terminal. (Previously explained...) Figure 17 The determination results of steps S36 to S38 are notified to output terminal 51 or output terminal 52. Figure 18 This represents an example of report screen 60 displayed on the output terminal at this time. Accompanied by Figure 17 The system determines whether a malfunction or fault is detected and notifies, for example, the output terminals registered for unloading. The notification application is then activated on the output terminal, whereby a report screen 60 is displayed. The system can be configured to notify the output terminal of the determination result even under normal determination conditions, if necessary.

[0160] also, Figure 18In addition to being displayed on the output terminal 51 when notified from the server 40, the output terminal 51 can also access the server 40 to download and display reports. In this case, the output terminal 51 can be used to specify the dump truck or the monitored object, and the report can be viewed for any monitored object whose status you want to know.

[0161] exist Figure 18 The example report screen 60 displays a message bar 61 and selection buttons 62-64. The message bar 61 displays messages indicating a malfunction in the monitored object, signs of a malfunction, or a normal state. Figure 18 The example illustrates a message indicating a potential malfunction. In this example, a message like "A potential malfunction has been detected at XXX on a dump truck (ID: XXX)" notifies which monitored object on which dump truck has experienced a malfunction.

[0162] Selecting buttons 62 and 63 displays recommended responses relative to the current situation. Figure 19 The button on screen 70 serves to visually or numerically indicate the probability of a malfunction. Selection button 62 changes color according to the situation, allowing the viewer to directly perceive the condition. In situations where a malfunction is not unexpected (e.g., a malfunction might occur within a week), selection button 62 is displayed, for example, in red or a red system (warning color). In situations where a malfunction is imminent (e.g., a malfunction might occur within a month), selection button 62 is displayed, for example, in yellow or a yellow system (caution color). In situations where there is a grace period but a malfunction is likely to occur soon (e.g., a malfunction might occur after a month), selection button 62 is displayed, for example, using blue, green, or a system (notification color). Operating selection button 62 displays... Figure 19 Screen 70 is shown.

[0163] Furthermore, regarding the determination of failure probability, although... Figure 17 While not explicitly stated in the procedure, for example, when comparing the interquartile range (ICM) with the decision value D2 in step S33, two thresholds α1 and α2 (α1 < α2) are pre-set relative to the difference between the ICM and the decision value D2. This allows for differentiating the severity of the omens, such as displaying a warning color if the difference is greater than α2, a warning color if it is greater than α1 but less than α2, and a notification color if it is less than α1. Similarly, in the decisions made in steps S34 and S35, the severity of the omens can be differentiated based on the degree of difference from the decision value.

[0164] The selection button 63 displays the loads of different components. For example, operating the selection button 63 will navigate to a screen (not shown) displaying the load status of each component or part, with each load status displayed in levels 0 to 5 according to its magnitude. This allows you to confirm the load status of each component or part.

[0165] If button 64 is pressed, a curve summarizing efficiency values ​​for the most recent specified period is displayed for the monitored object. The displayed curve is only required to confirm the extent of any abnormalities or signs in the monitored object's condition; its shape is not limited. For example, a curve that also displays... Figure 13 or Figure 14 The diagram shows a curve representing a pre-set anomaly or warning value for the monitored object, along with statistical data (a box plot of daily efficiency values). Alternatively, the anomaly or warning value may not be displayed; instead, a curve representing only efficiency values ​​(e.g., efficiency values ​​for each specified period, such as each day) may be shown. By displaying this curve on output terminal 51 or output terminal 52, managers or others can assess the status of the monitored object in addition to the judgment results from the processing device 20.

[0166] Screen 70 is displayed when the selection button 62 is operated. Figure 19 The message bar 71 and contact information bar 72 are displayed. Message bar 71 displays a message with detailed information explaining the situation. Figure 19 In the example, information about potential malfunctions within a week and maintenance recommendations are included. The contact information section 72 displays contact information (e.g., phone number, URL, email address) of relevant industry professionals or service personnel involved in the maintenance of the monitored object. If a phone number is displayed, and the output terminal 51 is a smartphone, a call can be made to that contact by tapping the contact information. If a URL is displayed, tapping or clicking the contact information will display its web page. If an email address is displayed, tapping or clicking the contact information will display a screen for creating an email address for that contact.

[0167] -Effect-

[0168] (1) According to this embodiment, the efficiency value of the monitored object is calculated, and the efficiency value data is collected and recorded for the monitored object, or output to the output terminal 51, etc. Compared with fuel consumption, the efficiency value is less affected by the driver's driving skills or the condition of the road surface, slope, etc., so the judgment of the condition of the monitored object (whether it is normal, abnormal, or has signs of abnormality, etc.) can be expected to be highly robust. However, when the load of the transmission system is low, as mentioned above, due to the high ratio of energy consumed by the auxiliary machine, the deviation of the calculation result of the efficiency value under the same driving skills or road conditions will also increase. Based on this view, in this embodiment, the load parameters (representatively the fuel injection quantity) that change corresponding to the load state of the transmission system 10 are calculated sequentially, and the efficiency value is calculated from the input and output energy of the monitored object when the load parameter is larger than the load judgment value X.

[0169] Therefore, the robustness of the efficiency value calculation results is improved, and by referring to the calculation results of the efficiency value, abnormalities in the transmission system 10 can be reasonably determined. For example, if an abnormality is found in a certain subsystem or transmission system, as long as the efficiency value reports of its constituent components can be confirmed, the component causing the abnormality can be specifically identified. Because it is possible to accurately determine the abnormality or its precursor in the transmission system 10, and which component or subsystem of the transmission system 10 is malfunctioning, appropriate measures can be taken in response to the fault or its precursor. Because faults in the transmission system 10 can be dealt with quickly and proactively, it also helps to suppress carbon dioxide emissions or fuel consumption. Furthermore, if there are signs of a fault in the efficiency value data, downtime at the time of the fault can be shortened by preparing to replace components in advance. Not only can abnormalities be identified, but the efficiency value of the monitored object also helps in the performance evaluation of the monitored object, and it has the advantage of allowing adjustments to be made for monitored objects where there is room for performance improvement.

[0170] (2) Figure 10 as well as Figure 11 As explained, by performing statistics based on data that differentiates efficiency values ​​using driving areas or data selected by the number of data points, the robustness of the efficiency values ​​can be further improved. In particular, as shown in this embodiment, when the efficiency value data is statistically analyzed using the vehicle controller 30, and anomaly detection is performed by the server 40, the data volume sent from the vehicle controller 30 to the server 40 is reduced, thereby suppressing communication costs or the computational load on the server 40.

[0171] (3) By Figure 13 or Figure 14The statistical data of such efficiency values ​​(e.g., box plots) are output together with the anomaly judgment value or the warning judgment value to the output terminal 51, etc., so that the efficiency value and the judgment value can be compared. Thus, managers and others can visually judge the status of the monitored object.

[0172] (4) The processing device 20 compares the abnormal judgment value or the omen judgment value with the efficiency value, and outputs the judgment result of the abnormality or omen of the monitored object to the recording or output terminal, so as to notify the manager and others of the status of the monitored object in real time.

[0173] (5) The efficiency value is statistically analyzed and the central value is calculated. If the central value is lower than the anomaly judgment value, it is determined that an anomaly has occurred in the monitored object. Therefore, compared with the instantaneous efficiency value calculated from the input and output energy at a certain point in time to determine the anomaly, the anomaly judgment can be performed with higher accuracy.

[0174] (6) The efficiency values ​​are statistically analyzed and the interquartile range is calculated. Thus, the difference between the maximum and minimum interquartile range (that is, the magnitude of the data deviation) can be used to detect the state (sign) of a monitored object that is about to fail even though it has not failed.

[0175] (7) By determining the extent to which the efficiency value of the same dump truck has decreased compared to past data, it is also possible to determine whether there are any abnormalities or signs in the monitored object.

[0176] (8) By judging how low the efficiency value of other dump trucks is, it is also possible to determine whether there is an abnormality or its precursor in the monitored object.

[0177] (9) By using the output terminal 51, which is a portable terminal with a display, the efficiency value or abnormality of the dump truck can be confirmed regardless of the location, and the faults of the monitored object can be dealt with quickly and flexibly.

[0178] (Second Implementation)

[0179] use Figures 20-23 The second embodiment of the present invention will be described. This embodiment is an example of computational efficiency when the slope is set as a load parameter and the vehicle travels in a slope area of ​​a certain value or higher (equivalent to the load determination value X or higher). The slope can be obtained from the signal of sensor S6, as long as terrain data is available, or it can be obtained from the position of the dump truck and terrain data. In addition, even without using terrain data or sensor S6, it is possible to estimate whether the slope (uphill gradient) of the road surface being traveled is a certain value or higher from, for example, the fuel injection amount. Hereinafter, a method for estimating whether the slope of the road surface being traveled is a certain value or higher will be described.

[0180] Figures 20-22This is an example of a plotting the average fuel injection amount for each grid cell within a positional coordinate system. One point represents one grid cell. Each plot is created based on the driving data of the same dump truck (one unit) over a single day. The horizontal axis of each plot represents latitude, the vertical axis represents longitude, and the density of the plotted points connected to the plotted driving trajectory indicates the amount of fuel injected.

[0181] Figure 20 This is an example of data on fuel injection volume filtered based on the condition that the truck is currently transporting cargo (with a load capacity greater than the specified value). As shown in the diagram, the dump truck travels to and from various locations within the area.

[0182] Figure 21 From Figure 20 The data extracted included examples where the number of sampled data points for each grid exceeded a specified value. The driving trajectories extracted from this map were presumed to be locations where dump trucks repeatedly drove at low speeds throughout the day.

[0183] Figure 22 From Figure 21 The data extracted included examples of fuel injection quantities exceeding a specified value. The driving trajectory extracted from this graph was presumed to be in areas with high acceleration resistance or high gradient resistance. In such cases, the transmission system is in a transitional state during acceleration due to increased acceleration resistance, and efficiency values ​​are prone to deviation. Therefore, even on non-gradient terrain, there may be instances where fuel injection quantities increase. Thus, using the same method as in the first embodiment, by filtering the data sample count within a specified time frame within the grid, conditions with significant efficiency deviations, such as deceleration followed by acceleration after high-speed driving, can be eliminated, allowing the extraction of driving data for gradient terrain. Furthermore, it is possible to target… Figure 22 The data was calculated for multiple dump trucks, and locations with an average efficiency data value higher than the specified value were presumed to be sloped areas.

[0184] Figure 23 This is a flowchart illustrating an example of a calculation procedure based on the efficiency value of a monitored object of a processing device. The flowchart in the same figure is consistent with that of the first embodiment. Figure 15 The flowchart corresponds to this. During the power supply period, or while the dump truck equipped with the vehicle controller 30 is in motion, the program stored in the memory 33 is read and repeatedly executed by the CPU 34 at a cycle of 0.1 seconds. Figure 23 The process. In this embodiment, via Figure 23 The processing calculates the efficiency value under high load conditions for each monitored object and records it along with the location code (described later) in memory 33. Thereafter, similar to the first embodiment, the following steps are performed: Figure 16 as well as Figure 17 The processing.

[0185] [Step S41]

[0186] When it begins Figure 23 In the process, the vehicle controller 30 inputs signals from sensors S1-S7 and the position measuring device S8 in step S41, and the CPU 34 calculates the current position of the dump truck based on the data received from the position measuring device S8. The position calculation method in this step is the same as... Figure 15 The same applies to step S14.

[0187] [Step S42]

[0188] In the next step S42, the on-board controller 30 infers the grid to which the dump truck's current position belongs and determines the position code of the inferred grid. The calculation method for the position code in this step is the same as... Figure 15 The same applies to step S15.

[0189] [Step S43]

[0190] In the next step S43, the on-board controller 30 uses the CPU 34 to determine whether the slope (load parameter) of the grid of the dump truck's current location code is greater than a specified value (load judgment value X for slope). This step is related to... Figure 15 The procedure corresponding to step S12. The slope data for the grid can be calculated based on terrain data (data obtained from prior measurements, etc.) or signals from sensor S6, or it can be used without terrain data or sensor S6. Figures 20-22 The calculation is performed using the method described herein. If the load parameter is greater than the load determination value X, the vehicle controller 30 transitions the program from step S43 to step S44. If the load parameter is less than the load determination value X, the vehicle controller 30 terminates the process. Figure 23 The process moves to the current processing loop and transitions the program to the next processing loop.

[0191] [Step S44]

[0192] If the program transitions to step S44, the vehicle controller 30 calculates the efficiency value by obtaining the ratio of input energy to output energy for each monitored object. The processing performed in this step is similar to... Figure 15 The same applies to step S13.

[0193] [Step S45]

[0194] In the next step S45, the vehicle controller 30 records the various efficiency values ​​calculated in step S44, the position codes attached to these efficiency values, and the time data sets to the memory 33. After completing step S45, the vehicle controller 30 terminates the process. Figure 15 The current processing loop of the process transitions the program to the next processing loop. The processing performed in this step is related to... Figure 15 The same applies to step S16.

[0195] The computational efficiency value in this embodiment can achieve the same effect as in the first embodiment. Furthermore, by comparing the results of driving in the same location, the accuracy of the determination can be improved through comparison with past data (step S34) or comparison with data from other vehicle bodies (step S35).

[0196] (Modified Example)

[0197] The above explanation illustrates an example of a configuration where the onboard controller 30 and server 40 share the functions of the processing unit 20. However, in cases where the efficiency value of a monitoring object is being evaluated for only one dump truck, a configuration where the onboard controller 30 performs all the functions of the processing unit can also be adopted. In this case, the first and second embodiments are capable of performing daily batch processing in real time. Figure 17 Advantages of the judgment processing. In the case of centralized management of multiple dump trucks, for example, as long as the dump trucks can communicate with each other, the on-board controller 30 of a specific dump truck can have the function of a server 40, and the unloading efficiency value of each dump truck can be evaluated.

[0198] Conversely, in the first and second embodiments, execution is carried out using the vehicle controller 30. Figure 16 The structure of the processing was illustrated as an example, but it can also be applied to... Figure 15 as well as Figure 23 The efficiency value calculated in the process is appended with the vehicle ID and sent to server 40, where it is then used for execution. Figure 16 The processing structure is optimized. In this case, the computational load on the vehicle controller 30 can be reduced. In addition, the rich efficiency data collected from each unloading can be analyzed in the management center, and the judgment values ​​such as the set time T1 or the set data number N1 can be adjusted and optimized in the server 40.

[0199] In the first and second embodiments, examples were given of determining both the anomaly of the monitored object and its precursors. However, it is also possible to determine only one aspect of the anomaly or its precursors. Furthermore, regarding the precursors of the anomaly... Figure 17 The example described illustrates the execution of three decision-making procedures (steps S33, S34, and S35), but a configuration that executes only one or two of these decisions, or one that adds further decisions, is also possible. The anomaly determination is not limited to comparison of the median value; a configuration that evaluates anomalies using other values ​​corresponding to the efficiency value (including the efficiency value itself) can also be used. Figure 17 The handling described is merely one example of using efficiency values ​​to determine anomalies or their precursors.

[0200] In addition, dump trucks were used as an example of vehicles managed by the vehicle body management system in the first and second embodiments, but other construction machinery such as wheeled excavators or wheeled loaders can also be managed using the vehicle body management system. It is assumed that wheeled vehicles that move a relatively short distance are used as the managed objects, but tracked vehicles can also be managed using the vehicle body management system if needed.

[0201] Explanation of reference numerals in the attached figures

[0202] 1. 1a, 1b Dump truck (body), 10 Transmission system (monitored object), 11 Engine (component, subsystem, monitored object), 12 Generator (component, subsystem, monitored object), 13 Rectifier (component, subsystem, monitored object), 14 Inverter (component, subsystem, monitored object), 15 Electric motor for driving (component, subsystem, monitored object), 20 Processing device, 30 On-board controller (processing device), 40 Server (processing device), 51, 52 Output terminal, D1 Anomaly judgment value, D2 Premonition judgment value, D3, D4 Judgment value, S1~S7 Sensors, S8 Position measuring device, X Load judgment value.

Claims

1. A vehicle body management system for managing a vehicle body having a transmission system comprising multiple components including an engine, the vehicle body management system being characterized by comprising: A processing device that calculates and records an efficiency value of a monitored object based on data detected by sensors located on the vehicle body, wherein the monitored object is the transmission system, a component of the transmission system, or a subsystem thereof; and An output terminal that outputs the efficiency value of the monitored object recorded by the processing device. The processing device calculates the load parameters of the transmission system. Determine whether the load parameter is greater than a preset load judgment value. If it is determined that the load parameter is greater than the load determination value, the input energy and output energy of the monitored object are calculated based on the data of the vehicle body detected by the sensor under the high load condition where the load parameter is greater than the load determination value, and the efficiency value of the monitored object under the high load condition is calculated based on the calculated input energy and output energy. The calculated efficiency value under the high load condition is recorded as the efficiency value of the monitored object.

2. The vehicle body management system according to claim 1, characterized in that, The processing device calculates the engine's fuel injection quantity, the load capacity of the cargo, or the road slope to use as the load parameters.

3. The vehicle body management system according to claim 1, characterized in that, The vehicle body is equipped with a position measuring device. The processing device calculates the position of the vehicle body based on the data received by the position measuring device and adds it to the efficiency value. The efficiency value is distinguished by the position of the vehicle body. Efficiency values ​​are collected for driving areas where the number of efficiency data points exceeds a set threshold within a set time period. The statistical data of the collected efficiency values ​​are then processed.

4. The vehicle body management system according to claim 3, characterized in that, The output terminal outputs the anomaly determination value or the precursor determination value for determining anomalies, which are pre-set for the monitored object, along with the statistical data.

5. The vehicle body management system according to claim 3, characterized in that, The processing device compares a pre-set anomaly detection value or a warning sign detection value for the monitored object with the statistical data to determine whether the monitored object has any anomalies or warning signs. The output terminal outputs the determination result of the processing device.

6. The vehicle body management system according to claim 5, characterized in that, The processing device calculates a median value based on the efficiency value to obtain the statistical data. If the central value is lower than the anomaly determination value, it is determined that an anomaly has occurred in the monitored object.

7. The vehicle body management system according to claim 5, characterized in that, The processing device calculates the interquartile range for the efficiency value to obtain the statistical data. If the difference between the maximum and minimum values ​​of the interquartile range exceeds the warning value, it is determined that there is an abnormal warning for the monitored object.

8. The vehicle body management system according to claim 3, characterized in that, The processing device acquires the difference between the statistical data and the statistical data calculated in the past. If the difference is greater than the judgment value, it is determined that there is an abnormality or an abnormal sign in the monitored object.

9. The vehicle body management system according to claim 3, characterized in that, The processing device acquires the difference between the statistical data and the statistical data calculated for other vehicle bodies. If the difference is greater than the judgment value, it is determined that there is an abnormality or an abnormal sign in the monitored object.

10. The vehicle body management system according to claim 1, characterized in that, The components of the transmission system include an engine, a generator driven by the engine, a rectifier that converts the output power of the generator into direct current, an inverter that converts the output power of the rectifier into three-phase alternating current, and a driving electric motor driven by the three-phase alternating current from the inverter.

11. The vehicle body management system according to claim 1, characterized in that, The output terminal is a portable terminal with a display.

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