WO1999009379A1 - A method for determining weight of load carried by a mining vehicle - Google Patents

A method for determining weight of load carried by a mining vehicle Download PDF

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Publication number
WO1999009379A1
WO1999009379A1 PCT/FI1998/000626 FI9800626W WO9909379A1 WO 1999009379 A1 WO1999009379 A1 WO 1999009379A1 FI 9800626 W FI9800626 W FI 9800626W WO 9909379 A1 WO9909379 A1 WO 9909379A1
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WO
WIPO (PCT)
Prior art keywords
load
fuzzy logic
neural network
vehicle
mining vehicle
Prior art date
Application number
PCT/FI1998/000626
Other languages
French (fr)
Inventor
Hannu Harmonen
Original Assignee
Tamrock Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tamrock Oy filed Critical Tamrock Oy
Priority to AU87339/98A priority Critical patent/AU8733998A/en
Publication of WO1999009379A1 publication Critical patent/WO1999009379A1/en

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/08—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for incorporation in vehicles
    • G01G19/12—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for incorporation in vehicles having electrical weight-sensitive devices

Definitions

  • the invention relates to a method for determining a weight of a load carried by a mining vehicle, the load weight being measured in the method by using separate sensors providing measurement signals on the basis of which the load weight is calculated.
  • Mining vehicles such as dumpers and load-haul-dump vehicles are used for transporting blasted rock from the blasting location to the dump location.
  • the operation takes place at a fairly rapid pace and at relatively short transport distances.
  • For the further process it is necessary to know the amount of blasted rock that has been transferred.
  • weighing can be performed, for instance as regards load-haul-dump vehicles, by measuring the cylinder pressure of e.g. lift cylinders, the cylinder pressure being then used, together with dimensions of the bucket mechanics and geometry, for calculating the weight of the amount of blasted rock on the load. Since in current systems measuring is calibrated, it is carried out by measuring the pressure first with an empty bucket and then with a load of a predetermined amount in the bucket.
  • the cylinder pressure of the platform lift cylinder and strain gauges are typically used, the strain gauges being located by the platform hinges, on hinge pins, on both sides of the dumper. Also in this case, the calibration is typically performed by using only one measured weight.
  • An object of the present invention is to provide a method for weighing, with a sufficient accuracy, a load of a moving vehicle.
  • the method of the invention is characterized in that measurement signals are processed by using a predetermined neural network, the load weight being obtained as a result from a base layer of the neural network; that at least some of the measurement signals and/or an output signal are processed on the basis of predetermined parameters by applying fuzzy logic, to ensure that the parameters are taken into account when an output signal value denoting the load is calculated.
  • An essential idea in the invention is that weighing is performed by using a solution implemented by means of a neural network and fuzzy logic whereby, in addition to the actual measurement signals received from a sensor, empirical data is used for correcting the result, the data being utilized in neural network computing by means of fuzzy logic. This enables a model to be created that takes into account at least the most significant factors affecting the weighing, allowing thus the most accurate weighing possible to be obtained.
  • Figure 1 is a schematic illustration of a dumper to which a method of the invention is applied;
  • Figure 2 is a schematic illustration of a load-haul-dump vehicle to which a method of the invention is applied; and
  • Figure 3 is a schematic illustration of an example of a neural network and fuzzy logic application, which can be applied for instance to determine a weight of a load of a dumper according to Figure 1.
  • Figure 1 is a schematic illustration of a dumper with a frame 1 moving on wheels and a platform 3 attached to the frame with joints 2 at the rear end.
  • lift cylinders 4 are placed between the platform and the frame 1 and when the platform is down, its front end rests on supports 5.
  • the dumper further comprises gravity-based sensors 6 that measure a horizontal tilt position of the frame 1 both in a longitudinal direction and a cross direction of the dumper.
  • the tilt position of the platform 3 in relation to the frame 1 can, in turn, be measured for instance either by using angular sensors located by the joints 2 or by measuring the volume of pressure fluid fed into the lift cylinders, the measured volume being then used, together with the geometry between the attachment points of the cylinders and the joints, for calculating a tilt angle.
  • FIG 2 in turn, illustrates a load-haul-dump vehicle with a frame 1 moving on wheels and a bucket 9 mounted to the frame by means of lifting arms 7 attached by joints 8, the bucket turning about joints 10 in relation to the lifting arms 7.
  • the load-haul-dump vehicle comprises a gravity-based tilt sensor 6, as shown in Figure 1 , the sensor measuring the tilt position of the load-haul-dump vehicle, both in its longitudinal direction and its cross direction, on the basis of the earth's gravity.
  • the position of the bucket 9 in relation to the vertical direction of the frame 1 can be determined, when the bucket 9 has been lifted in the most upright position possible by means of the tilt cylinder 11 , e.g. by using angular sensors located by the joints 8, the angular sensors and the geometry of the lifting arms 7 being utilized to calculate a lift height of the bucket.
  • the lift height can also be determined by measuring the volume of pressure fluid fed into the cylinder 4, which allows the lift height to be calculated on the basis of the length of the joints and the cylinder.
  • FIG 3 is a schematic illustration of a neural network and fuzzy logic structure applicable to the measurement of a load carried for instance by a dumper according to Figure 1 , the structure allowing the load to be measured also when the vehicle is moving.
  • sensors are used, two of the sensors being for instance strain gauges placed in a suitable position in relation to the joints 2 of the platform 3, on both sides of the dumper's frame.
  • Another sensor used is a pressure sensor measuring the pressure of the pressure fluid acting on the lift cylinder 4.
  • Measurement signals arriving from strain gauges 2a and 2b are transmitted through amplifiers 12 to the input of a neural network 13.
  • a measurement signal arriving from a pressure sensor 4a is transmitted to a fuzzy logic unit 14, which uses as a control parameter a temperature value arriving from a temperature sensor 4b of the pressure fluid of the cylinder 4.
  • a control parameter to the fuzzy logic unit 14 is also obtained a time instant from a timer 15. The time instant will be discussed later in connection with the operational description of the weighing procedure.
  • the signal received from the fuzzy logic unit 14 is further transmitted to the neural network 13, which processes the received signals in a predetermined manner and uses them to generate a value corresponding to the load weight.
  • the value is further transmitted to a second fuzzy logic unit 16, which uses as control parameters the vehicle tilt position provided by the tilt sensors 6 and a tilt value denoting the tilt position of the platform 3 in relation to the dumper frame 1 , the tilt value being determined as described above.
  • the fuzzy logic unit 16 determines a final load weight value, which is then transmitted to a weighing display or, through a computer system or a network, to process data files for the necessary processing.
  • the driver When the weighing is being carried out, the driver lifts the platform 3 during the driving so that it is detached from the supports 5 described in Figure 1. A signal light is then illuminated to indicate to the driver that the platform 3 rests only on the cylinders 4 and the joints 2.
  • the driver then pushes, after a while, a button activating the weighing, the timer 15 shown in Figure 3 measuring the time from the moment the platform 3 was detached from the supports 5 until the moment the driver pushes the weighing button.
  • the equipment calculates the tilt angle of the platform 3 in relation to the frame 1 , taking into account the tilt position of the frame denoted by the tilt sensors 6, and it then determines, in a manner according to a chart described in Figure 3, the weight of the blasted rock on the load.
  • the load on the vehicle i.e. on the dumper in this case
  • the tilt positions of the platform and the dumper in suitable predetermined positions and to place weights of a different size on the platform, preferably in a predetermined manner, to enable thus the impact of the different values on the weighing result to be detected.
  • the values obtained allow fuzzy logic to be controlled so that in practice the received signal measurement values can be corrected as desired, which ensures that the most correct weighing result possible can be obtained.
  • the weighing can be correspondingly performed for a load-haul- dump vehicle, the position of the bucket and other factors being then easy to take into account.
  • a load-haul-dump vehicle it is in principle possible to use a block chart shown in Figure 3, whereby the position of the bucket 9 in the vertical direction of the frame 1 and/or the inclination of its boom are taken into account by means of a control parameter 17 of the fuzzy logic processing the neural network output.
  • An essential feature is that the processing of the actual measurement signals is carried out by means of a predetermined neural network in such a way that when a load weight is determined, the impact of the signals as such can be taken into account as desired.
  • the measuring is calibrated by using suitable predetermined tilt and weight values, different tilt positions and loads and the position of the loads in the bucket, together with their various effects, can be taught to the neural network by applying fuzzy logic.

Abstract

A method for weighing a mining vehicle load by means of measurements made with different measurement sensors. In the method, measurement values obtained from the measurement sensors (2a, 2b, 4a, 4b, 6) are processed by applying fuzzy logic (14, 16) of a predetermined neural network (13) in connection with the processing of measurement signals.

Description

A METHOD FOR DETERMINING WEIGHT OF LOAD CARRIED BY A MINING VEHICLE
The invention relates to a method for determining a weight of a load carried by a mining vehicle, the load weight being measured in the method by using separate sensors providing measurement signals on the basis of which the load weight is calculated.
Mining vehicles, such as dumpers and load-haul-dump vehicles are used for transporting blasted rock from the blasting location to the dump location. The operation takes place at a fairly rapid pace and at relatively short transport distances. For the further process it is necessary to know the amount of blasted rock that has been transferred.
In prior art solutions weighing can be performed, for instance as regards load-haul-dump vehicles, by measuring the cylinder pressure of e.g. lift cylinders, the cylinder pressure being then used, together with dimensions of the bucket mechanics and geometry, for calculating the weight of the amount of blasted rock on the load. Since in current systems measuring is calibrated, it is carried out by measuring the pressure first with an empty bucket and then with a load of a predetermined amount in the bucket. As regards dumpers, the cylinder pressure of the platform lift cylinder and strain gauges are typically used, the strain gauges being located by the platform hinges, on hinge pins, on both sides of the dumper. Also in this case, the calibration is typically performed by using only one measured weight.
In a static situation, the measurement values obtained are fairly correct and the load on the vehicle can be weighed with a sufficient accuracy. A problem here is that in practice, due to rapid and short driving distances, the weighing must be performed during the driving, so tilt positions of the vehicle, bumps on the road and various other factors affect the weighing result and, in certain situations, a systematic error to a particular direction can easily develop. An accurate weighing could naturally be obtained with the vehicle stationary on a horizontal surface, but it would take up too much of the driving time to be economically rational.
An object of the present invention is to provide a method for weighing, with a sufficient accuracy, a load of a moving vehicle. The method of the invention is characterized in that measurement signals are processed by using a predetermined neural network, the load weight being obtained as a result from a base layer of the neural network; that at least some of the measurement signals and/or an output signal are processed on the basis of predetermined parameters by applying fuzzy logic, to ensure that the parameters are taken into account when an output signal value denoting the load is calculated.
An essential idea in the invention is that weighing is performed by using a solution implemented by means of a neural network and fuzzy logic whereby, in addition to the actual measurement signals received from a sensor, empirical data is used for correcting the result, the data being utilized in neural network computing by means of fuzzy logic. This enables a model to be created that takes into account at least the most significant factors affecting the weighing, allowing thus the most accurate weighing possible to be obtained.
The invention will be described in greater detail in the attached drawings in which
Figure 1 is a schematic illustration of a dumper to which a method of the invention is applied;
Figure 2 is a schematic illustration of a load-haul-dump vehicle to which a method of the invention is applied; and Figure 3 is a schematic illustration of an example of a neural network and fuzzy logic application, which can be applied for instance to determine a weight of a load of a dumper according to Figure 1.
Figure 1 is a schematic illustration of a dumper with a frame 1 moving on wheels and a platform 3 attached to the frame with joints 2 at the rear end. In order to empty the platform, lift cylinders 4 are placed between the platform and the frame 1 and when the platform is down, its front end rests on supports 5. The dumper further comprises gravity-based sensors 6 that measure a horizontal tilt position of the frame 1 both in a longitudinal direction and a cross direction of the dumper. The tilt position of the platform 3 in relation to the frame 1 can, in turn, be measured for instance either by using angular sensors located by the joints 2 or by measuring the volume of pressure fluid fed into the lift cylinders, the measured volume being then used, together with the geometry between the attachment points of the cylinders and the joints, for calculating a tilt angle. Figure 2, in turn, illustrates a load-haul-dump vehicle with a frame 1 moving on wheels and a bucket 9 mounted to the frame by means of lifting arms 7 attached by joints 8, the bucket turning about joints 10 in relation to the lifting arms 7. For the bucket to be tilted in relation to the lifting arms 7, it has a separate tilt cylinder 11 and to lift the bucket 9, there is also a lift cylinder 4 between the lifting arms 7 and the frame 1. In addition, the load-haul-dump vehicle comprises a gravity-based tilt sensor 6, as shown in Figure 1 , the sensor measuring the tilt position of the load-haul-dump vehicle, both in its longitudinal direction and its cross direction, on the basis of the earth's gravity. The position of the bucket 9 in relation to the vertical direction of the frame 1 can be determined, when the bucket 9 has been lifted in the most upright position possible by means of the tilt cylinder 11 , e.g. by using angular sensors located by the joints 8, the angular sensors and the geometry of the lifting arms 7 being utilized to calculate a lift height of the bucket. Alternatively, the lift height can also be determined by measuring the volume of pressure fluid fed into the cylinder 4, which allows the lift height to be calculated on the basis of the length of the joints and the cylinder.
Figure 3 is a schematic illustration of a neural network and fuzzy logic structure applicable to the measurement of a load carried for instance by a dumper according to Figure 1 , the structure allowing the load to be measured also when the vehicle is moving. For the actual measuring, sensors are used, two of the sensors being for instance strain gauges placed in a suitable position in relation to the joints 2 of the platform 3, on both sides of the dumper's frame. Another sensor used is a pressure sensor measuring the pressure of the pressure fluid acting on the lift cylinder 4. These sensors allow in principle the weight of a load to be measured and calculated with a sufficient accuracy on a horizontal surface in a static situation.
Measurement signals arriving from strain gauges 2a and 2b are transmitted through amplifiers 12 to the input of a neural network 13. A measurement signal arriving from a pressure sensor 4a is transmitted to a fuzzy logic unit 14, which uses as a control parameter a temperature value arriving from a temperature sensor 4b of the pressure fluid of the cylinder 4. As another control parameter, to the fuzzy logic unit 14 is also obtained a time instant from a timer 15. The time instant will be discussed later in connection with the operational description of the weighing procedure.
The signal received from the fuzzy logic unit 14 is further transmitted to the neural network 13, which processes the received signals in a predetermined manner and uses them to generate a value corresponding to the load weight. The value is further transmitted to a second fuzzy logic unit 16, which uses as control parameters the vehicle tilt position provided by the tilt sensors 6 and a tilt value denoting the tilt position of the platform 3 in relation to the dumper frame 1 , the tilt value being determined as described above. As a result, the fuzzy logic unit 16 determines a final load weight value, which is then transmitted to a weighing display or, through a computer system or a network, to process data files for the necessary processing.
When the weighing is being carried out, the driver lifts the platform 3 during the driving so that it is detached from the supports 5 described in Figure 1. A signal light is then illuminated to indicate to the driver that the platform 3 rests only on the cylinders 4 and the joints 2. The driver then pushes, after a while, a button activating the weighing, the timer 15 shown in Figure 3 measuring the time from the moment the platform 3 was detached from the supports 5 until the moment the driver pushes the weighing button. At the same time, the equipment calculates the tilt angle of the platform 3 in relation to the frame 1 , taking into account the tilt position of the frame denoted by the tilt sensors 6, and it then determines, in a manner according to a chart described in Figure 3, the weight of the blasted rock on the load.
To calibrate the weighing, it is possible to arrange, already in advance, the load on the vehicle, i.e. on the dumper in this case, and the tilt positions of the platform and the dumper in suitable predetermined positions and to place weights of a different size on the platform, preferably in a predetermined manner, to enable thus the impact of the different values on the weighing result to be detected. The values obtained allow fuzzy logic to be controlled so that in practice the received signal measurement values can be corrected as desired, which ensures that the most correct weighing result possible can be obtained.
The weighing can be correspondingly performed for a load-haul- dump vehicle, the position of the bucket and other factors being then easy to take into account. With a load-haul-dump vehicle, it is in principle possible to use a block chart shown in Figure 3, whereby the position of the bucket 9 in the vertical direction of the frame 1 and/or the inclination of its boom are taken into account by means of a control parameter 17 of the fuzzy logic processing the neural network output. In some cases it may be advantageous not to perform a momentary weighing but to take an average of for instance a predetermined length, whereby the measurement time can vary from only a few seconds to also longer periods of measurement.
The invention is presented in the above descriptions and drawings only by way of an example and it is in no way restricted to them. An essential feature is that the processing of the actual measurement signals is carried out by means of a predetermined neural network in such a way that when a load weight is determined, the impact of the signals as such can be taken into account as desired. When the measuring is calibrated by using suitable predetermined tilt and weight values, different tilt positions and loads and the position of the loads in the bucket, together with their various effects, can be taught to the neural network by applying fuzzy logic.

Claims

1. A method for determining a weight of a load carried by a mining vehicle, the load weight being measured in the method by using separate sensors providing measurement signals on the basis of which the load weight is calculated, characterized in that the measurement signals are processed by using a predetermined neural network, the load weight being obtained as a result from a base layer of the neural network; that at least some of the measurement signals and/or an output signal are processed according to predetermined parameters by applying fuzzy logic, to ensure that the parameters are taken into account when an output signal value denoting the load is calculated.
2. A method according to claim 1, characterized in that one of the sensors is a pressure sensor (4a) measuring a pressure of pressure fluid of a lift cylinder (4) of a load-carrying member, such as a platform or a bucket, and that the measurement signal received from the pressure sensor (4a) is processed by applying fuzzy logic.
3. A method according to claim 2, characterized in that one of the parameters used for controlling fuzzy logic is pressure fluid temperature.
4. A method according to any one of claims 1 to 3, characterized in that one of the control parameters used is horizontal tilt position of the mining vehicle.
5. A method according to claim 4, characterized in that a neural network output is processed by applying fuzzy logic.
6. A method according to claim 5, characterized in that the horizontal tilt position of the mining vehicle is used as a parameter controlling the fuzzy logic that processes the neural network output.
7. A method according to claim 5 or 6, characterized in that one of the control parameters used for the fuzzy logic that processes the neural network output is tilt position of the platform in relation to the mining vehicle or position of the mining vehicle bucket and/or inclination of the boom in relation to the vertical direction of the mining vehicle frame.
8. A method according to any one of the preceding claims, characterized in that the load weight is measured while the vehicle is moving.
9. A method according to any one of the preceding claims, characterized in that the operation of fuzzy logic is determined by measuring in advance the impact of loads of different weights and that of the positions of the vehicle, in a plural number of predetermined vehicle positions, on the value of the load weight.
10. A method according to any one of preceding claims, characterized in that the measurement is performed as a measurement period of a predetermined length and the load is calculated as an average load weight obtained during the measurement period.
PCT/FI1998/000626 1997-08-20 1998-08-13 A method for determining weight of load carried by a mining vehicle WO1999009379A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU87339/98A AU8733998A (en) 1997-08-20 1998-08-13 A method for determining weight of load carried by a mining vehicle

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FI973420 1997-08-20
FI973420A FI973420A (en) 1997-08-20 1997-08-20 It is still possible to determine the weight of a load carried by a mining vehicle

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WO1999009379A1 true WO1999009379A1 (en) 1999-02-25

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FI (1) FI973420A (en)
WO (1) WO1999009379A1 (en)
ZA (1) ZA987297B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2367370B (en) * 2000-09-28 2004-02-04 Caterpillar Inc Method and apparatus configured to perform viscosity compensation for a payload measurement system

Citations (7)

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Publication number Priority date Publication date Assignee Title
US4880142A (en) * 1987-05-12 1989-11-14 Fuji Photo Film Co., Ltd. Powder weighing mixer and method thereof
EP0617262A1 (en) * 1993-03-22 1994-09-28 Carl Schenck Ag Method for cancelling interference in a loss in weight feeder and device for carrying out the method
WO1995024616A1 (en) * 1994-03-10 1995-09-14 Koivisto, Marja-Liisa Method for measuring loads being directed to structures
EP0724142A1 (en) * 1995-01-27 1996-07-31 Sgs Thomson Microelectronics Sa Method for taring an electronic balance, for measuring with this balance or for measuring with another electronic apparatus
WO1997005408A1 (en) * 1995-07-26 1997-02-13 Siemens Aktiengesellschaft Circuitry for controlling a running or driving system of a motor vehicle
EP0797081A2 (en) * 1996-03-22 1997-09-24 Yazaki Corporation Automatic load measuring device
EP0855581A1 (en) * 1995-10-12 1998-07-29 Yazaki Corporation Device for calculating maldistribution of load on vehicle and device for calculating load on vehicle

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4880142A (en) * 1987-05-12 1989-11-14 Fuji Photo Film Co., Ltd. Powder weighing mixer and method thereof
EP0617262A1 (en) * 1993-03-22 1994-09-28 Carl Schenck Ag Method for cancelling interference in a loss in weight feeder and device for carrying out the method
WO1995024616A1 (en) * 1994-03-10 1995-09-14 Koivisto, Marja-Liisa Method for measuring loads being directed to structures
EP0724142A1 (en) * 1995-01-27 1996-07-31 Sgs Thomson Microelectronics Sa Method for taring an electronic balance, for measuring with this balance or for measuring with another electronic apparatus
WO1997005408A1 (en) * 1995-07-26 1997-02-13 Siemens Aktiengesellschaft Circuitry for controlling a running or driving system of a motor vehicle
EP0855581A1 (en) * 1995-10-12 1998-07-29 Yazaki Corporation Device for calculating maldistribution of load on vehicle and device for calculating load on vehicle
EP0797081A2 (en) * 1996-03-22 1997-09-24 Yazaki Corporation Automatic load measuring device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2367370B (en) * 2000-09-28 2004-02-04 Caterpillar Inc Method and apparatus configured to perform viscosity compensation for a payload measurement system

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Publication number Publication date
AU8733998A (en) 1999-03-08
FI973420A0 (en) 1997-08-20
FI973420A (en) 1999-02-21
ZA987297B (en) 1999-02-15

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