CN111503011B - Method and device for diagnosing inspection well pump - Google Patents
Method and device for diagnosing inspection well pump Download PDFInfo
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- CN111503011B CN111503011B CN201911338676.0A CN201911338676A CN111503011B CN 111503011 B CN111503011 B CN 111503011B CN 201911338676 A CN201911338676 A CN 201911338676A CN 111503011 B CN111503011 B CN 111503011B
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- 238000007689 inspection Methods 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000005856 abnormality Effects 0.000 claims abstract description 59
- 238000003745 diagnosis Methods 0.000 claims abstract description 56
- 230000002159 abnormal effect Effects 0.000 claims abstract description 44
- 238000010606 normalization Methods 0.000 claims abstract description 42
- 238000005070 sampling Methods 0.000 claims abstract description 24
- 238000011156 evaluation Methods 0.000 claims description 55
- 238000012545 processing Methods 0.000 claims description 41
- 238000010801 machine learning Methods 0.000 claims description 19
- 238000011084 recovery Methods 0.000 claims description 8
- 230000004913 activation Effects 0.000 claims description 5
- 238000002405 diagnostic procedure Methods 0.000 abstract description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 65
- 238000004891 communication Methods 0.000 description 20
- 230000001186 cumulative effect Effects 0.000 description 20
- 239000010865 sewage Substances 0.000 description 19
- 238000005259 measurement Methods 0.000 description 10
- 238000012806 monitoring device Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 238000012706 support-vector machine Methods 0.000 description 4
- 230000007423 decrease Effects 0.000 description 3
- 238000005086 pumping Methods 0.000 description 3
- 239000006185 dispersion Substances 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D13/00—Pumping installations or systems
- F04D13/12—Combinations of two or more pumps
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
- F04D15/0077—Safety measures
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
- F04D15/0088—Testing machines
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
- F04D15/02—Stopping of pumps, or operating valves, on occurrence of unwanted conditions
- F04D15/0209—Stopping of pumps, or operating valves, on occurrence of unwanted conditions responsive to a condition of the working fluid
- F04D15/0218—Stopping of pumps, or operating valves, on occurrence of unwanted conditions responsive to a condition of the working fluid the condition being a liquid level or a lack of liquid supply
- F04D15/0227—Lack of liquid level being detected using a flow transducer
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Control Of Positive-Displacement Pumps (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Control Of Non-Positive-Displacement Pumps (AREA)
- Sewage (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a method and a device for diagnosing a well pump, which can accurately diagnose abnormality due to various reasons even though the physical quantity is small. A diagnostic method of inspection well pump, set up two inspection well pumps in inspection well, repeat starting and stopping, the diagnostic method includes: a sampling step of sampling the operation time of each start of each inspection pump in a predetermined period and calculating the average operation time as a pair of characteristic values; a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system with one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump based on which side of a boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value point exists.
Description
Technical Field
The present invention relates to a diagnostic method and a diagnostic device for two inspection well pumps which are provided in an inspection well and repeatedly started and stopped.
Background
The inspection well pump device comprises: a water storage part for storing the sewage flowing in from the inflow pipe; a plurality of pumps for discharging the sewage stored in the water storage unit to the outflow pipe; a water level gauge for measuring the level of the sewage stored in the water storage unit; and a control panel provided with a control device for executing sewage conveyance control, wherein any pump is started to discharge sewage to the outflow pipe when the water level measured by the water level gauge reaches the pump start water level, and the pump is stopped when the water level reaches the pump stop water level.
In such manhole pump apparatuses, two pumps are usually provided, and a control device is configured so that each time sewage is transported, the pumps are alternately operated.
Patent document 1 proposes a pump field monitoring system including: a means for managing the value obtained by dividing the cumulative inflow amount of each of the pumps to be discharged from the plurality of pumps in the pumping field at predetermined time intervals by the operation time of each of the pumps as the average water discharge capacity of each of the pumps in accordance with time-series data; means for determining whether or not the average drainage capacity of each pump managed as the time-series data exceeds a preset drainage capacity range; and a unit for issuing an alarm when the average drainage capacity of each of the plurality of pumps exceeds the set drainage capacity range.
Patent document 2 discloses a control device for an underwater pump, comprising: a pressure-feed control unit that drives the underwater pump by operating the electromagnetic shutter when the stored water level detected by the water level sensor reaches the pump start water level; and an abnormality determination unit that determines whether or not there is an abnormality based on the operating state of the electromagnetic switch that drives the motor, the detection state of the current sensor that detects the current of the power supply line connected to the armature winding via the electromagnetic switch, and the stored water level, and if no current is detected by the current sensor and no decrease in the stored water level is detected by the water level sensor during operation of the electromagnetic switch, the abnormality determination unit determines that the motor that is automatically shut off is abnormal due to overheating.
Prior art literature
Patent literature
Patent document 1: japanese patent laid-open No. 2001-34338
Patent document 2: japanese patent application laid-open No. 2010-236191
The control device provided in the above-described conventional inspection well pump apparatus determines whether or not each pump is abnormal based on whether or not a physical quantity such as a driving current value of the pump, an operation time of the pump from a start water level to a stop water level, and a temperature of the pump exceeds a predetermined threshold. Further, in order to measure a physical quantity suitable for the type of the assumed abnormality, various sensors are required for the measurement process, and there is a very troublesome problem.
In addition, the threshold value for abnormality determination is also set in a unified manner because it is dependent on the environment of the inspection well in which the pump is provided. For example, in a region where the water intake amount per unit time is large and a region where the water intake amount per unit time is small, there is a problem in that it is difficult to accurately determine if the determination is made with a constant threshold value because the pump is deviated in the start frequency and the operation time.
In particular, in a remote monitoring system including a communication device and a server for managing operation data of pumps transmitted from the communication devices in a control panel of each manhole pump apparatus, the remote monitoring system can monitor an operation state by accessing the server from a terminal provided by a manager, the remote monitoring system has the following problems: in order to determine each abnormality of the pump provided in each inspection well pump device independently, it is necessary to transmit a very large number of physical quantities, and therefore, the number of sensors and the capacity of transmitted data are increased, and on the other hand, it is difficult to set an appropriate threshold value.
Disclosure of Invention
In view of the above-described problems, an object of the present invention is to provide a diagnostic method and a diagnostic apparatus for a inspection well pump, which can accurately diagnose abnormality due to various causes even with a small physical quantity.
In order to achieve the above object, a first feature of the inspection pump diagnostic method according to the present invention is a diagnostic method for two inspection pumps which are provided in an inspection well and are repeatedly started and stopped, the diagnostic method comprising: sampling, namely sampling the operation time of each start of each inspection well pump in a specified period and calculating the average operation time of each inspection well pump as a pair of characteristic values; a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system with one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump based on which side of a boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value point exists.
By normalizing the characteristic values in the normalization step, the characteristic values are composed of the respective average operation times calculated from the operation times of each activation of each inspection well pump sampled in time series in the sampling step, and it is possible to extract a pair of characteristic values excluding, for example, an influence due to an inherent machine difference of the inspection well pump, an influence of a setting environment, and the like as characteristic amounts, draw characteristic value points representing the pair of characteristic values in a two-dimensional coordinate system in the diagnosis step, and appropriately diagnose whether the inspection well pump is normal or abnormal using a preset boundary threshold value as an index.
The second feature is a diagnostic method of two inspection well pumps which are provided in an inspection well and repeatedly started and stopped, the diagnostic method comprising: sampling, namely sampling the operation time of each start of each inspection well pump in a specified period, calculating the average operation time of each inspection well pump, and taking one average operation time and the ratio of one average operation time to the other average operation time as a pair of characteristic values; a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump based on which of the boundary threshold values set in advance is present in the two-dimensional coordinate system at which the drawn characteristic value points are present.
The inspection well pump is characterized in that a pair of characteristic values, which are calculated from the operation time of each start of each inspection well pump sampled in time series in the sampling step, are normalized in the normalizing step, and a pair of characteristic values, which are obtained by excluding an influence of an inherent machine difference of the inspection well pump, an influence of a setting environment, and the like, are extracted as characteristic values, and a characteristic value point representing the pair of characteristic values is plotted in a two-dimensional coordinate system in the diagnosing step, and whether the inspection well pump is normal or abnormal is appropriately diagnosed by using a preset boundary threshold as an index.
In the third feature, the statistical data required for the normalization process used in the normalization step is calculated based on each characteristic value in the latest predetermined period when the normalization process is executed.
By normalizing a pair of characteristic values based on statistical data obtained from each characteristic value in a recent predetermined period, it is possible to eliminate the influence of time passage due to, for example, season fluctuation and to perform highly reliable diagnosis.
The fourth feature is characterized in that, in the diagnosis step, each time a characteristic value point is diagnosed as abnormal, a predetermined abnormal reference value is multiplied by a weighting coefficient based on a distance between the characteristic value point and the boundary threshold value and added to a current cumulative evaluation value to obtain a new cumulative evaluation value, and each time the characteristic value point is diagnosed as normal in the diagnosis step, a predetermined normal recovery evaluation value is subtracted from the current cumulative evaluation value to obtain a new cumulative evaluation value, and a final diagnosis is performed on the normal or abnormal condition of the inspection pump based on the final obtained cumulative evaluation value, wherein an initial value of the cumulative evaluation value is 0.
If a slight abnormality such as a normal abnormality is present after the abnormality is diagnosed in one diagnosis, there is also an abnormality which is continuously diagnosed as an abnormality and eventually causes a serious failure. In this case, the predetermined normal recovery evaluation value is subtracted from the predetermined normal recovery evaluation value every time the diagnosis is made as normal, and the cumulative evaluation value is calculated, and the normal or abnormal inspection well pump is finally diagnosed based on the cumulative evaluation value, thereby making it possible to perform diagnosis in consideration of the degree of abnormality such as the abnormality that requires recent maintenance.
In the fifth feature, in addition to any one of the first to fourth features, the feature point is that a diagnostic map is provided in which an outer side of the boundary threshold value set in the two-dimensional coordinate system is divided into a plurality of regions, each region being associated with one of the causes of the abnormality, and in the diagnosing step, the cause of the abnormality is diagnosed based on the region in which each characteristic value point is plotted.
The cause of the abnormality can be estimated from which region of the diagnostic map is divided outside the boundary threshold value representing the normal region, at which characteristic value points indicated by the pair of characteristic values after normalization.
In the sixth feature, in addition to any one of the first to fifth features, the boundary threshold is automatically generated by a machine learning device based on the characteristic value input as learning data in the diagnosing step.
If a plurality of pairs of characteristic values in a state where the inspection well pump is properly operated are input to the machine learning device, a boundary threshold value for identifying the normal and abnormal states is automatically generated without preparing teacher data for identifying the normal and abnormal states in advance and performing preparation such as machine learning.
A first feature of the inspection pump diagnostic device according to the present invention is a diagnostic device for two inspection pumps which are provided in an inspection well and are repeatedly started and stopped, the diagnostic device comprising: a normalization processing unit that normalizes a pair of characteristic values that are each composed of an average operation time calculated from an operation time for each activation of each inspection pump sampled within a predetermined period; and a diagnosis processing unit that draws a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, and performs a diagnosis of the normal or abnormal state of each inspection pump based on whether the drawn characteristic value point is located on the boundary threshold value preset in the two-dimensional coordinate system.
The second feature is a diagnostic device for two inspection pumps which are provided in an inspection well and repeatedly start and stop, the diagnostic device being characterized by comprising: a normalization processing unit that normalizes a pair of characteristic values that are configured from one of the average operation times and a ratio of the one average operation time to the other average operation time, the average operation time being calculated from the operation times of each activation of each inspection pump sampled within a predetermined period; and a diagnosis processing unit that draws a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnoses each inspection pump as to whether the inspection pump is normal or abnormal based on whether the drawn characteristic value point exists on the boundary threshold value preset in the two-dimensional coordinate system.
The third feature is characterized in that, each time each characteristic value point is diagnosed as abnormal at one time, the diagnosis processing unit multiplies a predetermined abnormal reference value by a weighting coefficient based on a distance between each characteristic value point and the boundary threshold value and adds the weighted coefficient to a current accumulated evaluation value to obtain a new accumulated evaluation value, and each time each characteristic value point is diagnosed as normal in the diagnosis step, subtracts the predetermined abnormal reference value from the current accumulated evaluation value to obtain a new accumulated evaluation value, and finally performs a final diagnosis of normal or abnormal of each inspection pump based on the finally obtained accumulated evaluation value, wherein an initial value of the accumulated evaluation value is 0.
The fourth feature is characterized by including a diagnostic map in which the outside of the boundary threshold set in the two-dimensional coordinate system is divided into a plurality of regions, each region being associated with one of the causes of the abnormality, and the diagnostic processing unit diagnosing the cause of the abnormality based on the region in which each characteristic value point is plotted.
In the fifth feature, in addition to any one of the first to fourth features, the diagnosis processing unit includes a machine learning device that automatically generates the boundary threshold value based on a pair of characteristic values input as learning data.
As described above, according to the present invention, a diagnostic method and a diagnostic apparatus for a inspection well pump are provided that can accurately perform abnormality diagnosis for various reasons even with a small physical quantity.
Drawings
FIG. 1 is an explanatory view of a inspection well pump.
FIG. 2 is an explanatory view of an abnormality diagnosis apparatus for a inspection well pump.
FIG. 3 is an explanatory diagram showing steps of an abnormality diagnosis method of a inspection well pump.
Fig. 4 (a) is an explanatory view of measurement data, and fig. 4 (b) is an enlarged explanatory view of a main part of measurement data.
Fig. 5 is an explanatory diagram of the normalization process.
Fig. 6 is an explanatory diagram of one diagnosis.
Fig. 7 is an explanatory diagram of the cumulative evaluation value and the final diagnosis.
Fig. 8 is an explanatory diagram of the diagnostic chart.
Symbol description
10. Inspection well pump equipment
PA, PB pump
18. 19 water level gauge
21. Control unit
22. Storage unit
24. Communication unit
30. Mobile terminal
40. Monitoring device
41. Communication unit
42. Data processing unit
44. Diagnostic part
46. Standardized processing part
48. Diagnostic processing unit
Detailed Description
The method and apparatus for diagnosing a inspection well pump according to the present invention will be described below.
Fig. 1 shows a inspection well pump apparatus 10. The inspection well pump device 10 includes: an inspection well 12 as a water storage portion that stores sewage flowing in from the sewage inflow pipe 11 on the upstream side; two pumps PA and PB for pumping the sewage stored in the manhole 12 to the sewage outflow pipe 13 on the downstream side; and water level gauges 18 and 19 for measuring the water level of the sewage stored in the manhole 12.
The first pumping pipe 15b, the first elbow pipe 15c, and the first horizontal pipe 15d are flange-connected to the discharge elbow pipe 15a of the first pump PA, respectively, and the first horizontal pipe 15d is flange-connected to the sewage outflow pipe 13 via the header 13 a. A check valve 15e is provided between the first suction pipe 15b and the first elbow pipe 15 c.
A second water suction pipe 17b and a second elbow pipe 17c are flange-connected to the discharge elbow pipe 17a of the second pump PB, respectively, and the second elbow pipe 17c is flange-connected to the sewage outflow pipe 13 via the header pipe 13 a. A check valve 17e is provided between the second suction pipe 17b and the second elbow pipe 17 c.
A water gauge 18 of the throw-in pressure type or bubble type is provided at the bottom of the manhole 12. The water level of the sewage stored in the manhole 12 is continuously detected by the water level gauge 18. The floating water level gauge 19 is provided as a standby water level gauge for detecting the abnormally high water level HHWL.
A control panel device 200 for housing a control panel 20 including a control unit 21 for performing sewage conveyance control for controlling pumps PA and PB to press-feed sewage stored in the manhole 12 to the sewage outflow pipe 13 is provided in the vicinity of the manhole 12.
The control panel 20 is provided with a control unit 21, a storage unit 22, and a communication unit 24. The storage unit 22 stores control information from the control unit 21, water level information from the water level gauges 18 and 19, and the like. The communication unit 24 includes: a transmitting unit for transmitting various information stored in the storage unit 22 to the remote monitoring device 40, and a receiving unit for receiving a control command from the monitoring device 40.
As a communication medium for connecting the communication unit 24 and the monitoring device 40, a wireless communication medium such as a mobile phone network is preferably used, the monitoring device 40 and the communication unit 24 are connected to each other via such a communication medium, and further, the portable communication terminal 30 (see fig. 2) provided by the manager of the inspection well pump device 10 and the monitoring device 40 can be connected to each other via the wireless communication medium.
The control panel 20 is connected to the pumps PA and PB via ac power supply lines L1 and L2, and the control panel 20 is connected to the water level gauges 18 and 19 via signal lines S.
When it is detected that the water level measured by the water level gauge 18 reaches the predetermined pump start water level HWL, the control unit 21 controls one of the pumps PA and PB to be supplied with power from the power supply line L1 in order to start the pump PA, and when it is detected that the water level reaches the pump stop water level LWL lower than the pump start water level HWL, the control unit 21 stops the supply of power to stop the one pump PA.
When the water level reaches the pump start water level HWL again, the control unit 21 controls the power supply line L2 to supply power for starting the other pump PB, and when the water level reaches the pump stop water level LWL, the control unit 21 stops the power supply to stop the pump PB. That is, the control unit 21 alternately controls the operation of the pumps PA and PB.
When the pump start water level HWL cannot be detected due to a failure of the water level gauge 18 or when a large amount of rainwater exceeding the drainage capacity of 1 pump flows into the manhole 12 due to concentrated heavy rain, the water level gauge 19 measures that the water level HHWL reaches an abnormally high level, and when the above is detected, the control unit 21 simultaneously operates both pumps PA and PB.
The control unit 21 samples the water level information detected by the water level gauges 18 and 19 at 1 minute intervals, for example, and stores the sampled water level information in the storage unit 22, and stores the operation information of the respective pumps PA and PB, the start and stop times, the operation time from the start to the stop, and the like in the storage unit 22.
As shown in fig. 2, the communication unit 24 provided in the control panel 20 of each inspection well pump device 10 is configured to transmit the water level information and the operation information stored in the storage unit 22 to the monitoring device 40 at predetermined intervals.
The monitoring device 40 functions as a diagnostic device for the inspection well pump device 10, and includes: a communication unit 41 that communicates with the communication unit 24 of each inspection well pump device 10 and the portable communication terminal 30 of the manager; a database DB storing water level information and operation information transmitted from the communication unit 24 of each inspection well pump device 10; a data processing unit 42 that exchanges data with the database DB; and a diagnosis unit 44 that diagnoses whether or not each inspection well pump device 10 is operating normally, based on the water level information and the operation information stored in the database DB.
The diagnostic unit 44 includes a normalization processing unit 46 and a diagnostic processing unit 48. The normalization processing unit 46 is configured to normalize a pair of characteristic values obtained from an operation time for a predetermined period of time, which is sampled in time series and indicates characteristics of each pump PA, PB. The average operation time of each pump can be calculated as a pair of characteristic values, and the average operation time of one pump and the ratio of the average operation time of one pump to the average operation time of the other pump can also be calculated as a pair of characteristic values.
The diagnosis processing unit 48 is configured to include a machine learning device that automatically generates a boundary threshold value for diagnosing whether the inspection pump is normal or abnormal based on a plurality of past data representing a pair of characteristic values inputted as learning data, and the machine learning device is configured to draw a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as the x-axis and the other as the y-axis, and to diagnose whether the inspection pump is normal or abnormal once based on which side of the boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value point exists. The machine learning device is composed of a computer executing a class of support vector machine (One-Class Support Vector Machine) algorithms.
The diagnosis processing unit 48 is configured to multiply a predetermined abnormality reference value by a weighting coefficient based on a distance between the characteristic value point and the boundary threshold value and add the value to a current accumulated evaluation value each time the characteristic value point is abnormal, and to subtract a predetermined normal recovery evaluation value from the current accumulated evaluation value to obtain a new accumulated evaluation value each time the characteristic value point is diagnosed as normal in one diagnosis, and to finally diagnose the normal or abnormal condition of the inspection well pump based on the finally obtained accumulated evaluation value, wherein an initial value of the accumulated evaluation value is 0.
The diagnostic map is provided, in which the outside of the boundary threshold value set in the two-dimensional coordinate system is divided into a plurality of regions, each region is associated with one of the causes of the abnormality, and the diagnostic processing unit 48 is configured to diagnose the cause of the abnormality based on the region in which each characteristic value point is plotted.
Fig. 3 shows a flow of a series of diagnostic processes performed by the monitoring apparatus 40. When measurement data of the amount of 1 day is received from each inspection well pump device 10 and finally stored in the database DB (SA 1), the respective operation times of the pumps PA and PB are extracted as measurement data for abnormality determination for each pump of each inspection well pump device 10, and the average operation time obtained by dividing the total of the operation times of the pumps for 1 day by the operation times is calculated for each pump PA and PB (SA 2).
As the feature quantity, a pair of characteristic values may be selected as the average operation time of each pump, or an average operation time of one pump and a ratio of the average operation time of one pump to the average operation time of the other pump may be selected as a pair of characteristic values. The statistics, that is, the average value and the variance value, required for the normalization processing are calculated from the average operation time stored in the database DB in the latest predetermined period, and the normalization processing is performed on the feature quantity of 1 day, which is the determination target (SA 3).
The normalized feature amount is input to the machine learning device, and a determination is made based on the set boundary threshold value (SA 5), and if the determination is abnormal, the cumulative evaluation value is added (SA 6) in consideration of the degree of abnormality, and if the determination is normal, the cumulative evaluation value is subtracted (SA 7).
If the cumulative evaluation value calculated by repeating such a process every day exceeds the cumulative abnormality threshold value (SA 8), the cause of the abnormality is determined based on a diagnostic chart showing the correlation with the cause of the abnormality in advance (SA 9), and an alarm as the content of the abnormal state is sent to a mobile terminal or the like owned by the manager together with the cause of the abnormality (SA 10). The alarm notification is transmitted as an email via a mailer provided in the communication unit 41.
The diagnostic unit 44 is described in detail below.
Fig. 4 (a) shows operation data of the inspection well pump device for 24 hours from 0 minutes at 0 am to 0 minutes at 0 day. The fluctuation of the water level, the operation timing and operation time of the pumps PA and PB, the current value of the pump PA, and the current value of the pump PB are shown in this order from the upper layer.
Fig. 4 (b) is an enlarged display for easily understanding the relationship between the fluctuation of the water level, the operation timings of the pumps PA and PB, and the operation time shown in fig. 4 (a). If the water level of the water stored in the inspection well reaches HWL, the pump PA is started, and if the water level is reduced to LWL, the pump PA is stopped. Then, the pump PB is started if the water level reaches HWL, and stopped if the water level decreases to LWL. During the water level decrease from HWL to LWL, either pump is started. When the amount of the pump is reduced or the amount of the sewage flowing into the manhole is large, the pump operation time becomes long.
Such water level information and operation information stored in the storage unit 22 of each inspection well pump device 10 are transmitted to the monitoring device 40 via the communication unit 24, and are stored in the database DB via the data processing unit 42.
The data processing unit 42 extracts the total value of the "pump operation times" and the "number of operations" of the pumps PA and PB at the time of starting from such data, and calculates the average operation time of the pumps as a pair of characteristic values or the ratio of the average operation time of one pump to the average operation time of the other pump as a pair of characteristic values based on the average operation time of 1 day obtained by dividing the total value of the "pump operation times" by the "number of operations", and transmits the calculated result to the normalization processing unit 46.
As shown in fig. 5, the normalization processing unit 46 normalizes the average value μ and the score for data by taking as a whole the feature values stored in the database DB during the last three monthsSigma dispersion 2 Calculation is performed to normalize each average operation time as a feature amount by the expression (x- μ)/σ for the feature amount x.
The statistical data (average value, dispersion value) required for the normalization processing is preferably calculated based on the measurement data set of the latest predetermined period when the normalization processing is executed, and the influence due to the lapse of time such as season fluctuation is eliminated, so that a highly reliable diagnosis can be performed.
When a pair of characteristic values including the characteristic amount "average operation time of each pump" after the normalization processing unit 46 performs the normalization processing or a pair of characteristic values including "average operation time of one pump and a ratio of average operation time of one pump to average operation time of the other pump" are input to the diagnosis processing unit 48, the normalized pair of characteristic values is plotted in a two-dimensional coordinate system having one of the pair of characteristic values as the x-axis and the other as the y-axis, and the inspection well pump is diagnosed once based on which of the boundary threshold values preset in the two-dimensional coordinate system the plotted characteristic value points exist.
By normalizing the characteristic value calculated based on the measurement values sampled in time series in the sampling step in the normalizing step, it is possible to extract the characteristic value excluding, for example, an influence due to a machine difference of the inspection well pump, an influence of the installation environment, or the like as the characteristic value, draw points representing these characteristic values, that is, two measurement data sets, as a plurality of points in time series in the two-dimensional coordinate system in the diagnosing step, and appropriately diagnose whether the inspection well pump device is normal or abnormal using a preset boundary threshold value as an index.
Fig. 6 shows a case where, in a two-dimensional coordinate system having a vertical axis (y-axis) as a ratio (T (PA)/T (PB)) of average operation time of each pump and a horizontal axis (x-axis) as an average operation time (T (PB)) of each pump, a substantially circular closed curve (indicated by a thick line) having an origin (average value of each value) of the two-dimensional coordinate system is set as a boundary threshold, and it is determined that the depicted characteristic value point is normal if the depicted characteristic value point is located inside the boundary threshold, and it is determined that the depicted characteristic value point is abnormal if the depicted characteristic value point is located outside the boundary threshold. Further, the characteristic value points may be plotted in a two-dimensional coordinate system in which the vertical axis (y-axis) is the average operating time of one pump and the horizontal axis (x-axis) is the average operating time of the other pump PB.
The diagnosis processing unit 48 includes a machine learning device that automatically generates the boundary threshold value based on the measurement data set input as the learning data. As the machine learning device, a computer or the like that executes algorithms such as a LOF (local outlier factor: local anomaly factor) method, an IF (Isolation Forest) method, an RC (Robust Covariance: robust covariance) method, or the like can be used in addition to the above-described support vector machine (One-Class Support Vector Machine).
By performing machine learning, a normal data space in which normal measurement data (training data) is mapped, that is, an internal space of the boundary threshold, is generated in the mapping space (feature space) shown in fig. 6. Fig. 6 shows an example in which a pair of characteristic values obtained for 1 year by the inspection well pump device at 100 where two pumps are provided are learned as learning data.
The diagnosis processing unit 48 multiplies the predetermined abnormality reference value Vnb by the weighting coefficient W based on the distance between the position of each characteristic value point and the boundary threshold value based on the following mathematical expression each time the characteristic value point is diagnosed as abnormal, calculates the cumulative evaluation value V by subtracting the predetermined normal recovery evaluation value Vpb each time each characteristic value point is diagnosed as normal in one diagnosis, and performs final diagnosis of the normal or abnormal condition of the inspection well pump device based on the cumulative evaluation value. The distance between the position of the characteristic value point and the boundary threshold value is the length of a normal line passing through the characteristic value point and distant from the boundary threshold value.
V＝Vnb×W－Vpb
In the present embodiment, vnb=1, and Vnb < Vpb < wmax×vnb is set.
If a slight abnormality such as a normal abnormality is present after the abnormality is diagnosed in one diagnosis, there is also an abnormality such as a serious failure that is eventually caused by the continued diagnosis of the abnormality. In this case, the value obtained by multiplying the predetermined abnormality reference value by the weighting coefficient based on the distance between the characteristic value point and the boundary threshold value is added each time the abnormality is diagnosed in one diagnosis, the predetermined normal recovery evaluation value is subtracted each time the abnormality is diagnosed as normal, the cumulative evaluation value thus obtained is calculated, and the final diagnosis is performed on the normal or abnormality of the machine based on the cumulative evaluation value, whereby the diagnosis can be performed in consideration of the degree of abnormality such as the abnormality that requires maintenance recently.
Fig. 7 shows a transition of the cumulative evaluation value. The cumulative evaluation value is evaluated once every predetermined period in which characteristic value points are plotted, and when abnormality determination is performed, the value vnb×w is added to the initial value 0, and when normal determination is performed, the value Vpb is subtracted. If the accumulated evaluation value exceeds a predetermined threshold value, in this example, 10, a final abnormality determination is performed, and the content is transmitted to a mobile terminal or the like provided in the manager. Further, the determination and the final determination are continued once even after the abnormality determination is performed.
Fig. 8 shows a diagnostic chart as an example. The outside of the boundary threshold value shown in the feature space shown in fig. 6 is divided into eight areas, each of which is associated with one of the causes of the abnormality. The diagnosis processing unit 48 diagnoses the cause of the abnormality based on the region in which each feature data is plotted.
For example, in the example of fig. 8, if the characteristic value point is drawn in the area 2, it is diagnosed that the pump PA is operating only for a long time, if the characteristic value point is drawn in the area 4, it is diagnosed that the external environment such as the water level meter is abnormal, if the characteristic value point is drawn in the area 5, it is diagnosed that both the pumps are operating for a long time, the inflow amount of sewage is increased, the junction of the discharge pipes is closed, and the external environment such as the external environment is abnormal, and if the characteristic value point is drawn in the area 8, it is diagnosed that the pump PB is operating only for a long time.
In the above example, the case where the average operation time of each pump or the ratio of the average operation time of one pump to the average operation time of the other pump is used as a pair of characteristic values has been described, but the pair of characteristic values are not limited to these data, and the operation time of each pump, the ratio of the operation time of each operation of one pump to the operation time of the other pump, the current value at the time of operation of each pump shown in fig. 4 (a), the average current value, the ratio of the average current values of both pumps, and the like can be appropriately set.
As described above, in the inspection well pump diagnosis method according to the present invention, two inspection well pumps are provided in an inspection well, and start and stop are repeated, wherein the inspection well pump diagnosis method includes: sampling, namely sampling the operation time of each start of each inspection well pump in a specified period and calculating the average operation time of each inspection well pump as a pair of characteristic values; a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system with one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump based on which side of a boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value point exists.
The present invention further includes: sampling, namely sampling the operation time of each start of each inspection well pump in a specified period, calculating the average operation time of each inspection well pump, and taking one average operation time and the ratio of one average operation time to the other average operation time as a pair of characteristic values; a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system with one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump based on which side of a boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value point exists.
The statistical data required for the normalization process used in the normalization step is calculated based on each characteristic value in the latest predetermined period when the normalization process is executed.
The diagnosing step is configured to multiply a predetermined abnormality reference value by a weighting coefficient based on a distance between the characteristic value point and the boundary threshold value and add the weighted coefficient to a current accumulated evaluation value each time the characteristic value point is diagnosed as abnormal, and to subtract a predetermined normal recovery evaluation value from the current accumulated evaluation value to obtain a new accumulated evaluation value each time the characteristic value point is diagnosed as normal in the diagnosing step, and to finally diagnose the normal or abnormal condition of the inspection pump based on the finally obtained accumulated evaluation value, wherein an initial value of the accumulated evaluation value is 0.
The diagnostic method is configured to include a diagnostic map in which the outside of a boundary threshold value set in a two-dimensional coordinate system is divided into a plurality of regions, each region being associated with one of the causes of an abnormality, and the diagnostic step diagnoses the cause of the abnormality based on the region in which each characteristic value point is plotted.
In the diagnosing step, the boundary threshold is preferably automatically generated by the machine learning device based on the characteristic value input as the learning data.
In the above-described embodiment, the whole of the diagnosis step is performed by the machine learning device, but only the generation of the boundary threshold may be performed by the machine learning device, or the diagnosis step may be performed using a predetermined boundary threshold without using the machine learning device.
The above embodiments are examples of the present invention, and the technical scope of the present invention is not limited to the description, and the specific configuration of each part represented by a pump, a water level gauge, etc., a threshold value set for abnormality determination, etc. can be appropriately changed within a range where the operational effects of the present invention are achieved.
Claims (8)
1. A method for diagnosing two inspection well pumps which are repeatedly started and stopped and are arranged in an inspection well, the method is characterized by comprising the following steps:
sampling, namely sampling the operation time of each start of each inspection well pump in a specified period, and calculating the average operation time of each inspection well pump as a pair of characteristic values;
a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and
a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system with one of the pair of characteristic values as an x-axis and the other as a y-axis, diagnosing the normal or abnormal state of each inspection pump once based on which side of a boundary threshold value preset in the two-dimensional coordinate system the drawn characteristic value points exist,
the statistical data required for the normalization process used in the normalization step is calculated based on only the characteristic values of the latest prescribed period when the normalization process is performed,
in the diagnosing step, the boundary threshold is automatically generated by a machine learning device based on a plurality of past data representing the pair of characteristic values input as learning data.
2. A method for diagnosing two inspection well pumps which are repeatedly started and stopped and are arranged in an inspection well, the method is characterized by comprising the following steps:
sampling, namely sampling the operation time of each start of each inspection well pump in a specified period, calculating the average operation time of each inspection well pump, and taking one average operation time and the ratio of one average operation time to the other average operation time as a pair of characteristic values;
a normalization step of normalizing the pair of characteristic values calculated in the sampling step; and
a diagnosis step of drawing a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, diagnosing the normal or abnormal state of each inspection pump once based on which of boundary thresholds preset in the two-dimensional coordinate system the drawn characteristic value points exist,
the statistical data required for the normalization process used in the normalization step is calculated based on only the characteristic values of the latest prescribed period when the normalization process is performed,
in the diagnosing step, the boundary threshold is automatically generated by a machine learning device based on a plurality of past data representing the pair of characteristic values input as learning data.
3. The method for diagnosing a inspection well pump according to claim 1 or 2, wherein,
in the diagnosing step, each time a characteristic value point is diagnosed as abnormal, a predetermined abnormal reference value is multiplied by a weighting coefficient based on a distance between the characteristic value point and the boundary threshold value and added to a current accumulated evaluation value to obtain a new accumulated evaluation value, and each time the characteristic value point is diagnosed as normal in the diagnosing step, a predetermined normal recovery evaluation value is subtracted from the current accumulated evaluation value to obtain a new accumulated evaluation value, and a final diagnosis is performed on the normal or abnormal condition of the inspection well pump based on the final obtained accumulated evaluation value, wherein an initial value of the accumulated evaluation value is 0.
4. The method for diagnosing a inspection well pump according to claim 1 or 2, wherein,
comprising a diagnostic map in which the outside of the boundary threshold set in the two-dimensional coordinate system is divided into a plurality of regions, each region being associated with one of the causes of the abnormality,
in the diagnosing step, the cause of the abnormality is diagnosed based on the region in which each characteristic value point is plotted.
5. A diagnostic device for inspection well pumps, which is provided in an inspection well and is provided with two inspection well pumps that are repeatedly started and stopped, is characterized by comprising:
a normalization processing unit that normalizes a pair of characteristic values, each of which is composed of an average operation time calculated from an operation time of each activation of each inspection pump sampled within a predetermined period; and
a diagnosis processing unit for drawing a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump once based on which of the boundary threshold values set in advance is present in the two-dimensional coordinate system at which the drawn characteristic value points are present,
the statistical data required for the normalization processing unit to perform the normalization is calculated based on only the characteristic values of the latest predetermined period when the normalization is performed,
the diagnosis processing unit includes a machine learning device that automatically generates the boundary threshold value based on a plurality of past data representing the pair of characteristic values inputted as learning data.
6. A diagnostic device for inspection well pumps, which is provided in an inspection well and is provided with two inspection well pumps that are repeatedly started and stopped, is characterized by comprising:
a normalization processing unit that normalizes a pair of characteristic values that are configured from one of the average operation times and a ratio of the one average operation time to the other average operation time, the average operation time being calculated from the operation time of each activation of each inspection pump sampled within a predetermined period; and
a diagnosis processing unit for drawing a normalized pair of characteristic values in a two-dimensional coordinate system having one of the pair of characteristic values as an x-axis and the other as a y-axis, and diagnosing the normal or abnormal state of each inspection pump once based on whether the drawn characteristic value point exists on the side of a boundary threshold value preset in the two-dimensional coordinate system,
the statistical data required for the normalization processing unit to perform the normalization is calculated based on only the characteristic values of the latest predetermined period when the normalization is performed,
the diagnosis processing unit includes a machine learning device that automatically generates the boundary threshold value based on a plurality of past data representing the pair of characteristic values inputted as learning data.
7. The diagnostic device of a inspection well pump according to claim 5 or 6, wherein,
the diagnosis processing unit multiplies a predetermined abnormality reference value by a weighting coefficient based on a distance between each characteristic value point and the boundary threshold value and adds the multiplied value to a current accumulated evaluation value to obtain a new accumulated evaluation value each time each characteristic value point is diagnosed as abnormal once, subtracts the predetermined abnormality reference value from the current accumulated evaluation value to obtain a new accumulated evaluation value each time each characteristic value point is diagnosed as normal once in diagnosis, and performs final diagnosis on the normal or abnormal condition of each inspection well pump based on the final obtained accumulated evaluation value, wherein an initial value of the accumulated evaluation value is 0.
8. The diagnostic device of a inspection well pump according to claim 5 or 6, wherein,
comprising a diagnostic map in which the outside of the boundary threshold set in a two-dimensional coordinate system is divided into a plurality of regions, each region being associated with one of the causes of an abnormality,
the diagnosis processing unit diagnoses the cause of the abnormality based on the region in which each characteristic value point is plotted.
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