Mine management system

Abstract

The present application aims to provide a mine management system capable of separately detecting abnormalities of a power transmission system and a load amount sensor of a mine dump truck. To this end, in a mine management system that manages a mine dump truck operating in a mine, a processing device that calculates a productivity index of the mine dump truck and performs statistics is provided, the processing device calculates consumed energy of the mine dump truck based on at least a vehicle speed, a road surface slope, and a load amount of the mine dump truck, calculates input energy of the mine dump truck based on at least one of a fuel injection amount, a trolley line power, and a battery power of the mine dump truck, and determines whether the load amount sensor or the power transmission system of the mine dump truck has an abnormality based on the consumed energy and the input energy.

Description

Technical Field

[0001] This invention relates to a mine management system for managing mine dump trucks operating in mines. Background Technology

[0002] In recent years, systems have been developed for collecting and analyzing data from mining machinery and calculating various management indicators based on the operating conditions of specific intervals along the path from unloading to the next unloading. As prior art literature disclosing such systems, Patent Document 1 is cited as an example. Patent Document 1 describes a system and method for calculating production efficiency indicators of mining machinery, such as fuel consumption or loading capacity per unit time, within specific intervals where adjacent road segments meet prescribed conditions.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: International Publication No. 2015 / 029229 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] However, while Patent Document 1 can detect increases in fuel consumption and decreases in load per unit time within a predetermined interval, it cannot monitor productivity decreases outside the predetermined interval. Furthermore, the load used in calculating productivity metrics (transportation volume per liter of fuel [T / L], transportation volume per unit time [T / h]) is mostly calculated based on the pressure of the suspension installed between the tire axle and the body of the dump truck. The device for calculating this load (load calculation device) is affected by changes in suspension oil and tire pressure, thus requiring calibration; however, methods for performing this calibration at appropriate intervals are not adequately considered in the prior art.

[0008] The present invention was made in view of the above-mentioned problems, and its object is to provide a mine management system capable of separately detecting anomalies in the power transmission system and the load sensor of a mine dump truck.

[0009] Methods for solving problems

[0010] To achieve the above objectives, a mining management system for managing mining dump trucks operating in a mine includes a processing device that calculates and statistically analyzes the productivity indicators of the mining dump trucks. This processing device calculates the energy consumed by the mining dump truck based at least on its speed, road slope, and load capacity. It also calculates the energy input of the mining dump truck based on at least one of its fuel injection quantity, trolley power, and battery power. Finally, it determines whether there are any abnormalities in the loading sensor or power transmission system of the mining dump truck based on the energy consumed and the energy input.

[0011] According to the present invention configured as described above, abnormalities in the power transmission system and the load sensor of a mining dump truck can be detected separately. As a result, when an abnormality is detected in the power transmission system of the mining dump truck, repair / replacement of the power transmission system can be implemented, or the operation of the mining dump truck can be reduced, thereby maintaining / improving mine productivity. Furthermore, when an abnormality is detected in the load sensor, mine productivity can be accurately managed by calibrating the load sensor.

[0012] Invention Effects

[0013] According to the mining system of the present invention, abnormalities in the power transmission system and the load sensor of the mining dump truck can be detected separately. Attached Figure Description

[0014] Figure 1 It is a diagram representing the overall picture of the mine management system.

[0015] Figure 2A This is a diagram illustrating an example of the power transmission system of a mining dump truck.

[0016] Figure 2B This is a diagram illustrating an example of the power transmission system of a mining dump truck.

[0017] Figure 2C This is a diagram illustrating an example of the power transmission system of a mining dump truck.

[0018] Figure 3 This is a diagram showing the changes in the status of mine dump trucks.

[0019] Figure 4 This is a graph showing statistical examples of the operating data of mine dump trucks.

[0020] Figure 5 This is a diagram illustrating an example of a functional block in a processing device.

[0021] Figure 6 This diagram illustrates an example of a method for calculating energy consumption using a vehicle model.

[0022] Figure 7 This is a flowchart illustrating an example of the processing performed by the vehicle model correction unit when correcting the road surface coefficient.

[0023] Figure 8 This is a flowchart of an example of the process performed by the vehicle model calibration department when calibrating the auxiliary power.

[0024] Figure 9 This is a diagram illustrating an example of a method for calculating the efficiency of each cycle based on the operating data of mine dump trucks.

[0025] Figure 10 This is a diagram illustrating an example of the relationship between efficiency when driving unloaded and when driving with load, as well as powertrain malfunctions and load sensor malfunctions.

[0026] Figure 11 This is a flowchart illustrating an example of the processing by the exception detection unit.

[0027] Figure 12 This is a graph showing the efficiency of unloaded driving and the efficiency of loaded driving, based on statistics for mining dump trucks.

[0028] Figure 13 This is an example diagram showing the relationship between efficiency and abnormal parts of the powertrain system during unloaded driving and sliding contact line driving.

[0029] Figure 14 This is a flowchart illustrating an example of the processing performed when the anomaly determination unit identifies an abnormal part of the power transmission system.

[0030] Figure 15 This is a graph showing the efficiency of unloaded driving and sliding contact line driving based on statistics for mining dump trucks.

[0031] Figure 16 This is a flowchart illustrating an example of the processing performed by the processing unit when it determines the cause of a decrease in productivity.

[0032] Figure 17 This is a graph showing examples of cycle efficiency statistics by route, by driver, and by mining dump truck. Detailed Implementation

[0033] Hereinafter, embodiments of the present invention will be described using the accompanying drawings.

[0034] Example 1

[0035] use Figures 1 to 12 The mine management system of the first embodiment of the present invention will be described.

[0036] Figure 1This diagram illustrates the overall situation of the mine management system in this embodiment. The mine management system 200 includes: a storage device 201 (e.g., a database) that collects location and operation information from multiple mining machines (dump trucks 101, excavators 102, bulldozers 103, etc.) operating in the same managed mine area 100; a processing device 202 (e.g., a server) that calculates the mine's productivity indicators based on the location and operation information of each mining machine 101-103, and determines the causes of productivity reduction, such as calibration information of the loading sensor 101a of the dump truck 101 and abnormalities in the power train of the dump truck 101; and a display terminal device 203 (e.g., a laptop computer, a portable terminal) that displays productivity indicators, causes of productivity reduction, calibration information, etc., in the form of an instrument panel. Here, it is preferable to send the operation data of each mining machine 101-103 to the mine management system 200 sequentially, but considering communication conditions and communication costs, sequential sending may not be possible. Therefore, the processing device 202 in this embodiment begins processing after buffering a certain amount of aggregated operational data. The amount of aggregated data can be determined, for example, based on the time equivalent to the longest past cycle from the load to the next load or the amount of data equivalent to the longest cycle.

[0037] Users of the mine management system 200 use information displayed on the display terminal device 203 (dashboard information) to detect potential reductions in mine productivity in advance and implement countermeasures based on the causes of the productivity reduction, thereby maintaining / managing mine productivity. For example, the mine's operations planner 301 can use the dashboard information to revise the operating plans of each mine dump truck 101. The operator instructor 302 can identify operators who need to improve their driving based on the dashboard information and provide driving guidance. The road maintenance personnel 303 can identify and repair road sections that are causing productivity reductions based on the dashboard information. The equipment maintenance personnel 304 can detect abnormalities in the power transmission system of the mine dump truck 101 based on the dashboard information and communicate with parts suppliers to prepare the necessary parts in advance. Furthermore, by combining weather information (history / forecast) and mineral prices (history / forecast) obtained via the Internet 400 with dashboard information, it is possible to issue revised instructions for the mining / maintenance plan to the mining manager 305, or to issue improvement instructions to the operations planner 301, operator supervisor 302, road maintenance personnel 303, and equipment maintenance personnel 304 to prevent a decrease in productivity. Moreover, the display terminal device 203 is not limited to a dashboard format; it can also be in the form of a report or email.

[0038] Figures 2A to 2C This is a diagram illustrating a structural example of the power transmission system of a mining dump truck 101. Figure 2AIn this structure, electricity generated by a generator driven by an engine or received from a trolley is supplied to the electric motor. Figure 2B The structure is a hybrid system equipped with a battery and a CONV (converter), which supplies the electric motor with electricity generated by a generator driven by the engine or electricity from the battery. Figure 2C The structure is an electric drive system that replaces the engine with a battery, supplying power from the battery or from a sliding contact line to the electric motor. The power transmission system components (engine, generator, INV (inverter), electric motor) are considered abnormal when their efficiency, calculated based on the input and output values ​​of each component, falls below a preset threshold. In the following description, the following explanation uses... Figure 2A The structure is an anomaly detection method for objects, but for Figure 2B or Figure 2C The same idea can be used to determine anomalies in the same structure.

[0039] Figure 3 This is a diagram showing the changes in the state of the mining dump truck 101. Figure 3 The upper section shows the loaded weight (load volume) of the transported ore calculated by the loading capacity calculation device, and the lower section shows the result of determining whether the vehicle is moving or stationary based on the vehicle speed detected by the vehicle speed sensor 101b (movement indicator). The cumulative load calculation device consists of a loading capacity sensor 101a installed on the suspension of the mining dump truck 101 and an on-board controller (not shown) that calculates the load volume based on the detection value of the loading capacity sensor 101a under specified vehicle conditions.

[0040] like Figure 3As shown, the driving cycle (hereinafter referred to as the cycle) of the mining dump truck 101 is roughly divided into four states: loaded (load), loaded driving (load driving), unloaded (unloading), and empty driving (empty driving). An example of the state determination method is given below. A specified value P1 is the threshold used to determine whether the state is loaded. When the load exceeds the specified value P1 while the truck is parked (T1), it is determined to be in a loaded state. After being determined to be in a loaded state, when the driving sign changes from parked to driving (T2), it is determined to be in a loaded driving state. When the load falls below the specified value P2 while the truck is parked (T3), it is determined to be in an unloading state. Then, when the driving sign changes to driving (T4), it is determined to be in an empty driving state. When the load exceeds the specified value P1 again while the truck is parked (T5), it is determined to be in a loaded state. By repeatedly performing this process, the cycle and four states (load, loaded driving, unloading, and empty driving) of the mining dump truck 101 can be determined. In this embodiment, a productivity index (e.g., the amount of fuel transported per liter [T / L]) is calculated for each cycle of the mining dump truck 101, and operational data is statistically analyzed according to the state for anomaly detection. Furthermore, in this embodiment, for ease of explanation, the period from one load to the next load is defined as one cycle; however, as long as it includes four consecutive states (load, load driving, unloading, and empty driving), the cycle can start from any state.

[0041] Figure 4 This is a graph showing statistical examples of the operating data of the mining dump truck 101. Figure 4 The upper section shows the load calculated by the load calculation device, the middle section shows the cycle fuel obtained by accumulating the engine injection amount per cycle, and the lower section shows the cycle travel distance obtained by accumulating the vehicle speed per cycle. Figure 4In this scenario, the loading capacity of cycle 1 is P1, the loading capacity of cycle 2 is P2, the fuel quantity of cycle 1 is L1, the fuel quantity of cycle 2 is L2, the travel distance of cycle 1 is D1, the travel distance of cycle 2 is D2, the travel time of cycle 1 is T1, and the travel time of cycle 2 is T2. The productivity index [T / L] of cycle 1 is calculated from P1 / L1, and the productivity index of cycle 2 is calculated from P2 / L2. However, they are difficult to compare because the cycle travel distances D1 and D2 or the cycle travel times S1 and S2 are different. In addition, if the travel path (especially the length of uphill roads) is different, the productivity index [T / L] will be very different for the same travel distance. Even for the same path, it varies with each cycle depending on factors such as excavator loading time, driving style (acceleration and deceleration), and road surface roughness. Therefore, it is difficult to obtain insights related to the management, maintenance, and improvement of productivity simply by calculating the productivity index [T / L] for each cycle. Therefore, in this embodiment, in addition to the productivity index [T / L], the state-based efficiency described later is also calculated, thereby accurately managing / maintaining productivity.

[0042] Figure 5 This is an example of a functional block diagram of the processing device 202. The processing device 202 includes a state determination unit 202a, an input energy calculation unit 202b, a consumption energy calculation unit 202c, a state-based efficiency calculation unit 202d, an anomaly determination unit 202e, and a vehicle model correction unit 202f. The processing device 202 is composed of a controller with arithmetic processing functions, an input / output interface for signal input and output with external devices, etc., and implements the functions of each unit by executing a program stored in a storage device such as ROM.

[0043] Status determination unit 202a passes Figure 3 The method described herein determines the state of the mining dump truck 101 based on vehicle speed and load. Furthermore, the input signal to the state determination unit 202a varies depending on the state of the mining dump truck 101 to be determined. For example, to determine the sliding contact line driving state, a sliding contact line voltage, etc., needs to be input.

[0044] The input energy calculation unit 202b calculates the input energy [kW / h] of the mining dump truck 101 based on the energy input to the power transmission system (fuel injection quantity, sliding contact line voltage, sliding contact line current, etc.). The input energy is, for example, the sum of engine heat (fuel injection quantity * fuel calorific value) and sliding contact line power (sliding contact line voltage * sliding contact line current).

[0045] The energy consumption calculation unit 202c calculates the energy consumption [kW / h] based on vehicle speed, height, and load capacity using the method described later.

[0046] The efficiency calculation unit 202d calculates the efficiency under specified conditions (cycle, no-load driving, loaded driving, etc.).

[0047] The anomaly determination unit 202e detects anomalies in the power transmission system and the load sensor 101a separately based on state efficiency using a method described later.

[0048] When the anomaly determination unit 202e does not determine that the load sensor 101a or the power transmission system is abnormal, the vehicle model correction unit 20f corrects the road surface coefficient and auxiliary power (parameters of the vehicle model used to calculate energy consumption) to bring the state-based efficiency within a specified range. According to this structure, robust anomaly determination can be achieved for changes in the road surface coefficient and auxiliary power that are significantly affected by the mine environment (weather, etc.), and the load sensor 101a is checked or calibrated at regular intervals when it is determined to be abnormal, thereby enabling accurate calculation of productivity indicators for each cycle.

[0049] Figure 6 This diagram illustrates an example of a method for calculating energy consumption using a vehicle model. The energy consumed during vehicle operation is obtained by multiplying the sum of air resistance, acceleration resistance, gradient resistance, and rolling resistance by the vehicle speed, and adding the basic energy consumption required for auxiliary equipment such as engine idling, air cooling fan, and cab air conditioning. Here, by obtaining vehicle speed, load, and height from vehicle operation data, the energy consumption corresponding to actual driving conditions can be calculated. Furthermore, air resistance also depends on air density, which varies depending on weather conditions, but its impact is smaller than rolling resistance and basic energy consumption; therefore, it is set here to a value determined solely by vehicle speed. Gradient resistance is calculated based on the total vehicle weight (including load) and the road gradient. In this embodiment, the road gradient is calculated based on the time-varying height; however, if a tilt angle sensor is installed on the vehicle body, it can also be calculated based on the vehicle's tilt angle.

[0050] In addition to the calculations described above, to further improve the accuracy of the estimated energy consumption, the road surface coefficient (used to calculate rolling resistance) and the auxiliary power (used to calculate basic energy consumption) are calibrated in a timely manner based on actual operating data using methods described later. By using such a vehicle model, energy consumption can be calculated regardless of the specific model of the mining dump truck 101.

[0051] Figure 7 This is a flowchart illustrating an example of the processing performed by the vehicle model correction unit 202f when correcting the road surface coefficient. The steps will be explained sequentially below.

[0052] First, the vehicle model calibration unit 202f determines whether there is an abnormality in the load sensor 101a or the powertrain system (step S701). If the determination in step S701 is "no" (there is an abnormality in the load sensor 101a or the powertrain system), the process ends.

[0053] If the determination in step S701 is "yes" (load sensor 101a or powertrain system is not abnormal), the state efficiency (stable driving efficiency) of the powertrain system in the driving area where the efficiency is approximately fixed is calculated (step S702). The stable driving efficiency is the state efficiency calculated when the vehicle is traveling at a fixed speed (a state with almost no acceleration resistance). In this embodiment, for simplicity, the state efficiency during load climbing or sliding contact line driving is used to correct the road surface coefficient of the entire mine area. However, GPS coordinates can also be used to identify a specified path or specified section, and the road surface coefficient can be corrected according to the identified path or section.

[0054] Next, in step S702, it is determined whether the efficiency during stable driving is less than the specified value R1 (the minimum efficiency expected during stable driving) (step S703). If the determination in step S703 is "yes" (the efficiency during stable driving is less than the specified value R1), the road surface coefficient is adjusted to the increasing side (step S704), and the process ends.

[0055] If step S703 determines "No" (efficiency during stable driving is above the specified value R1), then it is determined whether the efficiency during stable driving is above the specified value R2 (the maximum efficiency envisioned during stable driving) (step S705). If step S705 determines "No" (efficiency during stable driving is below the specified value R2), the process ends. If step S705 determines "Yes" (efficiency during stable driving is above the specified value R2), the lateral correction road surface coefficient is reduced, and the process ends.

[0056] In this way, if the efficiency during stable driving is lower than the pre-determined minimum efficiency R1, it is judged as a deterioration of road conditions, leading to an increase in the road surface coefficient. Conversely, if the efficiency during stable driving is higher than the pre-determined maximum efficiency R2, it is judged as a recovery of road conditions, leading to a decrease in the road surface coefficient. This enables robust anomaly detection for changes in road conditions caused by weather and other factors. Furthermore, by setting upper and lower limits for the road surface coefficient, over-correction of the energy consumed by the road surface coefficient can be prevented.

[0057] Figure 8 This is a flowchart illustrating an example of the processing performed by the vehicle model calibration unit 202f when calibrating auxiliary power. The steps will be explained sequentially below.

[0058] First, the vehicle model calibration unit 202f determines whether there is an abnormality in the load sensor 101a or the powertrain system (step S801). If the determination in step S801 is "no" (there is an abnormality in the load sensor 101a or the powertrain system), the process ends.

[0059] If the determination in step S801 is "yes" (load sensor 101a or power transmission system is not abnormal), the power consumption of the auxiliary machine becomes the dominant state efficiency during idling (efficiency during idling) (step S802).

[0060] Next, in step S802, it is determined whether the efficiency during idling is less than the specified value I1 (the minimum efficiency expected during idling) (step S803). If the determination in step S803 is "yes" (the efficiency during idling is less than the specified value I1), the auxiliary power is adjusted to the boost side (step S804), and the process ends.

[0061] If step S803 determines "No" (efficiency at idle is above the specified value I1), then it is determined whether the efficiency at idle is above the specified value I2 (the maximum efficiency expected at idle) (step S805). If step S805 determines "No" (efficiency at idle is below the specified value I2), the process ends. If step S805 determines "Yes" (efficiency at idle is above the specified value I2), the auxiliary power is adjusted to the reduced side (step S805), and the process ends.

[0062] In this way, when the efficiency during idling is lower than the pre-designed minimum efficiency I1, the auxiliary power is increased; when the efficiency during idling is higher than the pre-designed maximum efficiency I2, the auxiliary power is decreased. This enables robust anomaly detection for fluctuations in auxiliary power caused by significant load changes due to environmental factors.

[0063] Figure 9 This is a diagram illustrating an example of a method for calculating the efficiency of each cycle based on the operating data of the mine dump truck 101. Figure 9 The upper section shows the load capacity, the middle section shows the input energy, and the lower section shows the consumed energy. The specified values ​​for the load capacity, P1 and P2, are as follows: Figure 4 As explained in the text, the input energy is the value obtained by accumulating the engine heat input (fuel injection quantity × fuel heat generation) and the conductor rail power (conductor rail voltage × conductor rail current) of the mining dump truck 101 over each cycle. Figure 6The vehicle model described in the diagram is used to calculate energy consumption. The efficiency of each cycle is calculated by dividing the energy consumed by the energy input at the end of each cycle. In the diagram, the efficiency of cycle 1 is OE1 / IE1, and the efficiency of cycle 2 is OE2 / IE2. Since the above vehicle model is robust to driving conditions and weather conditions, anomalies in the efficiency of each cycle can be detected regardless of who is driving on which path.

[0064] Figure 10 This diagram illustrates an example of the relationship between efficiency during unloaded driving and efficiency during loaded driving, as well as abnormalities in the powertrain system and the load sensor. In this embodiment, efficiency during unloaded driving is considered low if it is less than a predetermined value 1 (the minimum efficiency envisioned during unloaded driving), and is considered standard if it is 1 or higher. Similarly, efficiency during loaded driving is considered low if it is less than a predetermined value 2 (the minimum efficiency envisioned during loaded driving), is considered standard if it is 2 or higher and 3 or lower, and is considered high if it is 3 or higher.

[0065] like Figure 10 As shown, when efficiency is low during unloaded driving, it is determined that there is an abnormality in the powertrain system. Furthermore, in this state, when efficiency is standard or high during loaded driving, it is determined that the load is detected excessively due to an abnormality in the load sensor 101a. This is because, due to the abnormality in the powertrain system, efficiency decreases during unloaded driving; on the other hand, because the load is detected excessively, the energy consumption is calculated to be greater than the actual amount, thus capturing the phenomenon of increased efficiency during loaded driving.

[0066] When the efficiency is standard during unloaded driving, if the efficiency is low during loaded driving, it is determined that the load is detected too low due to an abnormality in the load sensor 101a. If the efficiency is standard during loaded driving, it is determined that the powertrain system is normal. If the efficiency is high during loaded driving, it is determined that the load is detected too high due to an abnormality in the load sensor 101a. Here, the specified value 1 and specified value 2 are approximately the same (however, compared to unloaded driving, the efficiency of the powertrain system is higher during loaded driving, so it is preferable that specified value 2 is slightly larger than specified value 1), and specified value 3 is set to a value larger than specified value 1 or specified value 2 (e.g., the maximum efficiency of the powertrain system).

[0067] Figure 11 This is a flowchart illustrating an example of the processing by the exception detection unit 202e. The steps will be explained sequentially below.

[0068] First, the anomaly determination unit 202e determines whether the state efficiency (efficiency during unloaded driving) is less than a specified value of 1 (step S1101).

[0069] If the determination in step S1101 is "yes" (efficiency is less than the specified value of 1 when driving without load), the abnormality of the powertrain system is notified to the equipment maintenance personnel 304 (step S1102). At this time, the operation planner 301 may also be notified to avoid using the vehicle.

[0070] Next, in step S1102, it is determined whether the state efficiency (efficiency during load driving) is greater than or equal to a specified value of 2 (step S1103). If the determination in step S1103 is "no" (efficiency during load driving is less than the specified value of 2), the process ends.

[0071] If the determination in step S1103 is "yes" (efficiency is above the specified value of 2 when driving with load), it is determined that the load is detected too high due to the abnormality of the load sensor 101a. The productivity index calculated in the abnormality is excluded from the statistical processing, and the equipment maintenance personnel 304 are notified to perform calibration of the load sensor 101a, or the mine dump truck 101 is remotely instructed to perform calibration of the load sensor 101a (step S1104).

[0072] If the determination in step S1101 is "No" (efficiency is above the specified value 1 when driving without load), then determine whether the efficiency when driving with load is less than the specified value 2 (step S1105).

[0073] If the determination in step S1105 is "yes" (efficiency is less than the specified value 2 when driving with load), it is determined that the load is detected too low due to the abnormality of the load sensor 101a. The productivity index calculated in this abnormality is excluded from the statistical processing, and the equipment maintenance personnel 304 are notified to perform calibration of the load sensor 101a, or the mine dump truck 101 is remotely instructed to perform calibration of the load sensor 101a (step S1106).

[0074] If the determination in step S1105 is "No" (efficiency during load driving is below the specified value 2), then determine whether the efficiency during load driving is above the specified value 3 (step S1107). If the determination in step S1107 is "No" (efficiency during load driving is less than the specified value 3), then the process ends.

[0075] If the determination in step S1107 is "yes" (efficiency is above the specified value of 3 when driving with load), it is determined that the load is detected too high due to the abnormality of the load sensor 101a. The productivity index calculated in this abnormality is excluded from the statistical processing, and the equipment maintenance personnel 304 are notified to perform calibration of the load sensor 101a, or the mine dump truck 101 is remotely instructed to perform calibration of the load sensor 101a (step S1108), and the process ends.

[0076] Through the above processing, the load sensor 101a can be calibrated at appropriate intervals, and by excluding the productivity index of the period in which the load is detected as too small / too large from the statistical processing, the statistics / management based on inaccurate loads can be prevented.

[0077] Figure 12 This is a graph showing examples of the efficiency during unloaded driving and the efficiency during loaded driving, based on statistics for dump trucks. Figure 12 In the example, the no-load driving efficiency and loaded driving efficiency for a specified period (e.g., the most recent week) are displayed in a box plot for each mining dump truck. The median values ​​of the no-load driving efficiency and loaded driving efficiency are used, and... Figure 10 , Figure 11 The method described herein is used for anomaly detection, which can suppress the influence of driver / driving path on the detection results, thus achieving more robust anomaly detection than anomaly detection performed on a cycle-by-cycle basis.

[0078] exist Figure 12 In the example, the efficiency of units Tr5, Tr21, and Tr1 during unloaded operation is less than the specified value of 1, therefore it is determined to be an abnormality in the power transmission system. Furthermore, the efficiency of unit Tr1 during loaded operation is greater than the specified value of 2, therefore it is also determined to be an abnormality (overload detection) of the load capacity sensor 101a. Unit Tr6, because its efficiency during unloaded operation is greater than the specified value of 1 and its efficiency during loaded operation is less than the specified value of 2, is determined to be an abnormality (underload detection) of the load capacity sensor 101a. Unit Tr3, because its efficiency during loaded operation is greater than the specified value of 3, is determined to be an abnormality (underload detection) of the load capacity sensor 101a. For example... Figure 12 As shown, by arranging and displaying each mine dump truck 101 according to its status efficiency, it is possible to compare the degree of abnormality of each mine dump truck 101. Therefore, when multiple mine dump trucks 101 malfunction, it becomes easy to determine which mine dump truck 101 to calibrate or maintain.

[0079] (Summarize)

[0080] In this embodiment, the mine management system 200, which manages the mine dump truck 101 operating in the mine, includes a processing device 202 that calculates and statistically analyzes the productivity indicators of the mine dump truck 101. The processing device 202 calculates the energy consumed by the mine dump truck 101 based at least on the vehicle speed, road slope, and load of the mine dump truck 101, and calculates the energy input of the mine dump truck 101 based on at least one of the fuel injection amount, conductor rail power, and battery power of the mine dump truck 101. Based on the energy consumed and the energy input, it determines whether there is any abnormality in the load sensor 101a or the power transmission system of the mine dump truck 101.

[0081] According to this embodiment configured as described above, abnormalities in the power transmission system and the load sensor 101a of the mining dump truck 101 can be detected separately. As a result, when an abnormality is detected in the power transmission system of the mining dump truck 101, maintenance / replacement of the power transmission system can be performed, or the operation of the mining dump truck can be reduced, thereby maintaining / improving mine productivity. Furthermore, when an abnormality is detected in the load sensor 101a, calibration of the load sensor 101a can be performed, enabling accurate management of mine productivity.

[0082] Furthermore, in this embodiment, the processing device 202 determines the state of the mining dump truck 101 based at least on its speed and load capacity. It calculates the ratio of consumed energy to input energy (i.e., state efficiency) based on the state of the mining dump truck 101, and determines whether the load capacity sensor 101a or the power transmission system is malfunctioning based on the comparison result of the state efficiency with a predetermined value. Therefore, it is possible to determine whether the load capacity sensor 101a or the power transmission system is malfunctioning, independent of driving conditions and the environment.

[0083] Furthermore, in this embodiment, the processing device 202 adjusts the parameter (auxiliary power) used to calculate the energy consumption so that the state-by-state efficiency of the mining dump truck 101 when it is idling converges within a specified range (I1 to I2). Thus, robust anomaly detection can be achieved, for example, in response to changes in auxiliary power.

[0084] Furthermore, the processing device 202 in this embodiment adjusts the parameters (road surface coefficient) used to calculate the energy consumption so that the state-based efficiency of the mining dump truck 101 when it is in a stable driving state converges within a specified range (R1 to R2). Thus, for example, robust anomaly detection can be achieved in response to changes in the road surface coefficient.

[0085] Furthermore, in this embodiment, the processing device 202 determines that the loading capacity sensor 101a is abnormal when the state efficiency of the mining dump truck 101 during unloaded driving is lower than a first predetermined value (predetermined value 1) and the state efficiency of the mining dump truck 101 during loaded driving is a second predetermined value (predetermined value 2) or higher; or when the state efficiency during unloaded driving is a first predetermined value (first predetermined value) or higher and the state efficiency during loaded driving deviates from a predetermined range (predetermined value 2 to predetermined value 3). Therefore, the abnormality of the loading capacity sensor 101a can be detected based on the state efficiency of the mining dump truck 101 during both unloaded and loaded driving.

[0086] Furthermore, in this embodiment, when the processing device 202 determines that the load capacity sensor 101a is malfunctioning, it determines that the load capacity sensor 101a needs to be calibrated. Therefore, the load capacity sensor 101a can be calibrated in a timely manner.

[0087] Furthermore, the processing device 202 in this embodiment excludes the productivity index calculated during the period when the load sensor 101a is determined to be abnormal from the statistical processing. This prevents the statistical / management of productivity indexes based on inaccurate load amounts.

[0088] Example 2

[0089] use Figures 13 to 15 The mine management system of the second embodiment of the present invention will be described below. The structure of the power transmission system envisioned in this embodiment is the same as that in the first embodiment. Figure 2A (As shown). Furthermore, in this embodiment, it is assumed that no abnormality of the load sensor 101a is detected by the method described in the first embodiment.

[0090] Figure 13 This is a diagram illustrating the relationship between efficiency and abnormal parts of the powertrain during unloaded driving and sliding contact line driving. According to... Figure 2A As shown in the structure, the state efficiency under the condition of receiving power from the sliding contact line is almost unaffected by the engine and generator. Therefore, as Figure 13 As shown, by combining the state-based efficiency determination during sliding contact line driving with the state-based efficiency determination during unloaded driving, the abnormal part of the powertrain system can be identified as one of the sliding contact line system, engine / generator, and motor / inverter.

[0091] In this embodiment, if the efficiency during no-load driving is less than a predetermined value of 1 (the minimum efficiency envisioned for no-load driving), it is determined to be low efficiency during no-load driving; if the efficiency during no-load driving is greater than or equal to the predetermined value of 1, it is determined to be standard efficiency during no-load driving. Furthermore, if the efficiency during conductor rail operation is less than a predetermined value of 4 (the minimum efficiency envisioned for conductor rail operation), it is determined to be low efficiency during conductor rail operation; if the efficiency during conductor rail operation is greater than or equal to the predetermined value of 4, it is determined to be standard efficiency during conductor rail operation. If the efficiency during no-load driving is low, it is determined to be an abnormality in the motor / inverter; if the efficiency during conductor rail operation is standard, it is determined to be an abnormality in the engine / generator system. If the efficiency during no-load driving is standard, if the efficiency during conductor rail operation is low, it is determined to be an abnormality in the conductor rail system; if the efficiency during conductor rail operation is standard, it is determined to be a normal powertrain system. As a result, the scope of investigation for anomaly determination becomes more limited, and the pre-ordering of parts becomes easier. Recovery time from anomalies is shortened, thus enabling the rapid elimination of productivity reductions caused by power transmission system anomalies.

[0092] Figure 14 This is a flowchart illustrating an example of the processing performed by the anomaly determination unit 202e when it identifies an abnormal location in the powertrain system. The steps will be explained sequentially below.

[0093] First, the anomaly determination unit 202e determines whether the efficiency during unloaded driving (efficiency during unloaded driving) is less than the specified value 1 (step S1401).

[0094] If the determination in step S1401 is "yes" (efficiency is less than the specified value 1 when driving without load), then determine whether the efficiency (sliding contact line efficiency) when driving with the sliding contact line is less than the specified value 4 (step S1402).

[0095] If the determination in step S1402 is "yes" (the sliding contact line efficiency is less than the specified value of 4), the abnormality of the motor / inverter is notified to the equipment maintenance personnel 304 (step S1403), and the process ends. In the event of a genuine abnormality, more fuel and sliding contact line power are consumed compared to normal, so for example, the operation planner 301 may be notified to request a reduction in the frequency of vehicle use or a shorter driving distance.

[0096] If the determination in step S1402 is "No" (the efficiency of the conductor rail is above the specified value of 4), the abnormality of the engine / generator is notified to the equipment maintenance personnel 304 (step S1404), and the process ends. Since the fuel loss is minimal when power is supplied from the conductor rail system in the event of a true abnormality, the reduction in productivity caused by the abnormality can be minimized by prompting the operation planner 301 to allocate the vehicle to a path that uses the conductor rail more frequently.

[0097] If the determination in step S1401 is "No" (efficiency is above the specified value 1 when driving without load), then determine whether the efficiency of the sliding contact line is less than the specified value 4 (step S1405). If the determination in step S1405 is "No" (efficiency of the sliding contact line is above the specified value 4), then the process ends.

[0098] If the determination in step S1405 is "yes" (the efficiency of the conductor rail is less than the specified value of 4), the abnormality of the conductor rail system is notified to the equipment maintenance personnel 304 (step S1406), and the process ends. In the event of a true abnormality, everything except the conductor rail system is normal. Therefore, by having the operation planner 301 assign appropriate vehicles to paths that do not use the conductor rail, the reduction in productivity can be minimized.

[0099] Figure 15 This is a graph showing the efficiency of unloaded driving and sliding contact line driving, based on statistics of mining dump trucks, within a specified period. Figure 15 In the example, the no-load driving efficiency and loaded driving efficiency for a specified period (e.g., the most recent week) are displayed in a box-type diagram for each mining dump truck. The median values ​​of the no-load driving efficiency and loaded driving efficiency are used to... Figure 13 , Figure 14 The method described in the document is used for anomaly detection.

[0100] exist Figure 15 In the example, Unit Tr5 was determined to have a motor / inverter malfunction because its efficiency during no-load operation was less than the specified value of 1 and its efficiency during sliding contact line operation was less than the specified value of 4. Unit Tr21 was determined to have an engine / generator malfunction because its efficiency during no-load operation was less than the specified value of 1 and its efficiency during sliding contact line operation was greater than the specified value of 4. Unit Tr6 was determined to have a sliding contact line system malfunction because its efficiency during no-load operation was greater than the specified value of 1 and its efficiency during sliding contact line operation was less than the specified value of 4. Figure 15 As shown, by displaying the state efficiency of all machines, the abnormality status of all mine dump trucks 101 operating in the mine can be monitored. Equipment maintenance personnel 304 can appropriately adjust the repair implementation plan, and operation planner 301 can appropriately adjust the vehicle allocation plan. Thus, the reduction in production efficiency when abnormalities occur can be minimized.

[0101] According to this embodiment configured as described above, since the fault location of the power transmission system of the mining dump truck 101 has been identified, the mine's productivity can be maintained by implementing appropriate countermeasures to the fault location.

[0102] Example 3

[0103] use Figure 16 and Figure 17The mine management system according to the third embodiment of the present invention will be described. Furthermore, in this embodiment, it is assumed that no abnormality was detected in the load sensor 101a using the method described in the first embodiment.

[0104] Figure 16 This is a flowchart illustrating an example of the process performed by the processing unit 202 when it determines the cause of a decrease in productivity. The steps will be explained sequentially below.

[0105] First, the processing device 202 performs statistical analysis on the cycle efficiency calculated for each cycle along the path, and determines whether the statistical representative value (e.g., the median value, the average value) is lower than the predetermined value R (step S1601). The predetermined value R can be set as the minimum value of the cycle efficiency envisioned in the corresponding path, or it can be set as the minimum value of the cycle efficiency envisioned in the representative path.

[0106] If, in step S1601, it is determined that the statistical representative value is lower than the specified value R (yes), the display terminal device 203 displays information (path ID) for identifying the maintenance candidate path (step S1602). As a result, the road maintenance personnel 303 can perform maintenance on that path, and the operation planner 301 can re-plan the operation schedule to reduce the number of trips on that path.

[0107] If, in step S1601, it is determined that the statistical representative value is above the predetermined value R (no), then in step S1602, the driver performs statistics on the aforementioned cycle efficiency and determines whether the statistical representative value is less than the predetermined value D (step S1603). The predetermined value D can be set as a pre-conceived minimum value of cycle efficiency.

[0108] If, in step S1603, it is determined that the statistical representative value is lower than the specified value D (yes), the information (driver ID) of the driver whose cycle efficiency is lower than the specified value D is displayed on the display terminal device 203 (step S1604). Thus, the operator instructor 302 can provide driving guidance to the driver.

[0109] If, in step S1603, the statistical representative value is determined to be above the predetermined value D (No), then in step S1604, the aforementioned cycle efficiency is statistically analyzed for mining dump trucks, and it is determined whether the statistical representative value is lower than the predetermined value V (step S1605). The predetermined value V is set as a pre-defined minimum value for cycle efficiency. If, in step S1605, the determination is "No" (the statistical representative value is above the predetermined value V), the process ends.

[0110] If the determination in step S1605 is "yes" (the statistical representative value is smaller than the specified value V), the information (dump truck ID) used to identify the mining dump truck with a cycle efficiency lower than the specified value V is displayed on the display terminal device 203 (step S1606), and the process ends. Thus, the equipment maintenance personnel 304 can perform maintenance on the mining dump truck.

[0111] Figure 17 This is a diagram showing an example of cycle efficiency statistics by route, driver, and mining dump truck. The cycle efficiency calculated over a specified period (e.g., one week) by route, driver, or mining dump truck is displayed sequentially from the lowest median value, making it easy to identify routes, drivers, or mining dump trucks 101 with low cycle efficiency. The displayed statistical results can be arbitrarily changed; for example, only the statistical results with the lowest median value can be displayed. Furthermore, by displaying thresholds (specified values ​​R, D, V), it is possible to notify road maintenance personnel 303, operator instructors 302, or equipment maintenance personnel 304 at appropriate times. Additionally, route IDs, driver IDs, and dump truck IDs can all be correlated with data on the overall map representing the work site. Taking route IDs as an example, the displayed route IDs are not limited to a single one; multiple route IDs can be displayed. For example, if multiple routes to the same location exist, multiple objects can be identified simultaneously on the map, allowing for the selection of the optimal route ID from the displayed options. Taking driver ID as an example, when multiple drivers have IDs lower than the specified value D at any given time, notifying them all at once is sometimes more efficient than notifying each driver individually. Furthermore, route ID, driver ID, and dump truck ID can be displayed on the map individually or simultaneously. The display can be customized not only according to user specifications but also arbitrarily changed to pre-set times, whenever a problem occurs, or combinations thereof. The display order of route, driver, and mining dump truck can also be arbitrarily changed; only desired objects can be displayed, or all can be displayed together. Moreover, when notifications are issued more than twice, the notification history and analysis results (improvement points / issues) compared to that history can be displayed simultaneously. Notifications can be sent in any form that the recipient can confirm, such as via email or voice message to a smartphone. This allows for the rapid elimination of the causes of productivity reduction.

[0112] (Summarize)

[0113] In this embodiment, the processing device 202 calculates the state-based efficiency for each path traveled by the mining dump truck 101, and determines that the road surface of the path whose statistical representative value of the state-based efficiency is lower than a predetermined value R is abnormal. Furthermore, in this embodiment, the processing device 202 calculates the state-based efficiency for each driver of the mining dump truck 101, and determines that the driving of the driver whose statistical representative value of the state-based efficiency is lower than a predetermined value D has a problem. Additionally, in this embodiment, the processing device calculates the state-based efficiency for each mining dump truck 101, and determines that the mining dump truck 101 whose statistical representative value of the state-based efficiency is lower than a predetermined value R is abnormal.

[0114] Based on this embodiment configured as described above, the cause of the decrease in mine productivity has been determined, and therefore, by implementing appropriate countermeasures to address the cause, the mine productivity can be maintained.

[0115] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the above embodiments and includes various modifications. For example, the above embodiments are examples described in detail for the purpose of easily understanding and illustrating the present invention, and are not limited to having all the structures described. In addition, a part of the structure of another embodiment may be added to the structure of a certain embodiment, a part of the structure of a certain embodiment may be deleted, or a part of the structure of another embodiment may be replaced.

[0116] Symbol Explanation

[0117] 100…Mine area, 101…Mine dump truck, 101a…Loading capacity sensor, 101b…Vehicle speed sensor, 102…Excavator, 103…Bulldozer, 200…Mine management system, 201…Storage device, 202…Processing device, 202a…Status determination unit, 202b…Input energy calculation unit, 202c…Consumption energy calculation unit, 202d…Status efficiency calculation unit, 202e…Anomaly determination unit, 202f…Vehicle model calibration unit, 203…Display terminal device, 301…Operation planner, 302…Operator instructor, 303…Road maintenance personnel, 304…Equipment maintenance personnel, 305…Mining supervisor, 400…Internet.

Claims

1. A mine management system for managing mine dump trucks operating in a mine, characterized in that, The mine management system has a processing device for calculating and statistically analyzing the productivity indicators of mine dump trucks. The processing device performs the following processing: The energy consumption of the mining dump truck should be calculated based at least on its speed, road gradient, and load capacity. The energy input of the mining dump truck is calculated based on at least one of the fuel injection quantity, the power of the sliding contact line, and the power of the battery. The determination of whether the mining dump truck is in a loaded driving state or an unloaded driving state is based at least on its speed and load capacity. The ratio of energy consumed to energy input, calculated based on the state of the mining dump truck, is known as the state efficiency. If the efficiency per state when the mining dump truck is in an unloaded driving state is above a first predetermined value, and the efficiency per state when the mining dump truck is in a loaded driving state deviates from a predetermined range, then the loading sensor detecting the loading amount of the mining dump truck is determined to be abnormal. If the efficiency per state in the unloaded driving state is lower than a first predetermined value and the efficiency per state in the loaded driving state is lower than a second predetermined value, the power transmission system of the mining dump truck is determined to be abnormal. If the efficiency per state in the unloaded driving state is lower than a first predetermined value and the efficiency per state in the loaded driving state is higher than a second predetermined value, then both the load sensor and the power transmission system are determined to be abnormal.

2. The mine management system according to claim 1, characterized in that, The processing device adjusts the parameters used to calculate the energy consumption so that the state efficiency of the mining dump truck when it is idling converges to a specified range.

3. The mine management system according to claim 1, characterized in that, The processing device adjusts the parameters used to calculate the energy consumption so that the state efficiency of the mining dump truck in a stable driving state converges within a specified range.

4. The mine management system according to claim 1, characterized in that, If the load capacity sensor is determined to be abnormal, the processing device determines that the load capacity sensor needs to be calibrated.

5. The mine management system according to claim 1, characterized in that, The processing device excludes the productivity index calculated during the period when the load sensor is determined to be abnormal from the statistical processing.

6. The mine management system according to claim 1, characterized in that, The processing device calculates the state-based efficiency according to the path traveled by the mining dump truck, and determines that the road surface of the path where the statistical representative value of the state-based efficiency is lower than a specified value is abnormal.

7. The mine management system according to claim 1, characterized in that, The processing device calculates the state-based efficiency based on the drivers of the mining dump trucks, and determines that if the statistical representative value of the state-based efficiency is lower than a specified value, the driver's driving has a problem.

8. The mine management system according to claim 1, characterized in that, The processing device calculates the state efficiency based on the mining dump truck, and determines that the mining dump truck with a state efficiency statistical representative value lower than a specified value is abnormal.

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