CN117371978A - Water supply project equipment fault tracing method based on Internet of things platform - Google Patents
Water supply project equipment fault tracing method based on Internet of things platform Download PDFInfo
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Abstract
The invention discloses a water supply project equipment fault tracing method based on an internet of things platform, which comprises the following steps of: the method comprises the steps that an Internet of things platform establishes a water supply project equipment data analysis system, and states of all equipment and reported data are analyzed in real time; generating an alarm aiming at equipment which is offline and has heartbeat data and no sensor data, and carrying out alarm filtering at the same time; establishing a root cause fault tracing rule base, analyzing an alarm event and positioning an alarm component; establishing a fault tracing knowledge base for storing all fault reasons currently known by each component of the front-end sensing equipment; and analyzing historical monitoring data before the failure of the alarm component based on a Mann-Kendall algorithm, and combining a failure tracing knowledge base to locate the failure cause. According to the equipment monitoring data based on the Internet of things platform, equipment fault reasons are automatically traced according to the equipment fault phenomena and the monitoring data, manual timely checking is guided, and the equipment operation and maintenance efficiency can be remarkably improved.
Description
Technical Field
The invention relates to the field of water supply monitoring equipment fault tracing, in particular to a water supply project equipment fault tracing method based on an Internet of things platform.
Background
The basic driving force of the internet of things platform is monitoring data of various front-end sensing devices, and service works such as water supply engineering and water supply network operation monitoring, pipe explosion diagnosis, water loss analysis and the like can be developed based on the monitoring data.
The water supply project internet of things platform is connected with a large number of front-end sensing devices such as flow, pressure, water quality and water level, and the problems of device monitoring data reporting caused by aging, battery power failure, hardware faults, RTU software faults, severe weather and the like are often not found in time, and an existing fault tracing mode after finding also needs experienced operation and maintenance personnel to conduct on-site troubleshooting and identify fault reasons, is too dependent on manual experience and has hysteresis in processing, and further affects the operation monitoring, pipe explosion diagnosis, water loss analysis and other businesses of the project water supply project.
Therefore, a water supply project equipment fault tracing method based on the internet of things platform needs to be developed.
Disclosure of Invention
Aiming at the problems existing in the existing equipment fault identification and tracing and troubleshooting process, the invention discloses an equipment fault tracing method based on an Internet of things platform, which utilizes the Internet of things platform to analyze equipment data and timely identify fault equipment, and combines an established root fault tracing rule base, a fault tracing knowledge base and a Mann-Kendall algorithm to locate possible reasons of fault components for tracing and troubleshooting so as to guide manual rapid troubleshooting and disposal.
In order to solve the technical problems, the invention provides a water supply project equipment fault tracing method based on an internet of things platform, which comprises the following steps:
s1: the method comprises the steps that an Internet of things platform is accessed to water supply project equipment data, a data analysis system is established, and online and offline states of all the equipment and reported monitoring data are analyzed in real time;
s2: generating an alarm aiming at equipment which is offline, has heartbeat data, has no sensor data and has abnormal sensor data, and filtering the alarm;
s3: establishing a root-cause fault tracing rule base, analyzing alarm events, and positioning a root-cause fault component through a plurality of subordinate alarm events;
s4: establishing a fault tracing knowledge base for storing all fault reasons currently known by each component of the front-end sensing equipment;
s5: based on the Mann-Kendall algorithm, historical monitoring data before the alarm part fails are analyzed, and the algorithm analysis is combined with a failure tracing knowledge base to locate specific failure reasons.
In the above technical solution, in the step S1, the platform of the internet of things of water supply item accesses a large number of front-end sensing devices of flow, pressure, water quality and water level, and besides completing the data access of the devices, a device data analysis system needs to be established to analyze whether the offline and online states of the devices and the reported data of the devices are normal or not in real time.
In the above technical solution, the step S1 includes:
(1) On-line and off-line status analysis of devices
Reporting frequency of monitoring equipment of a water supply project is generally reported for a plurality of times every day, and considering that some monitoring equipment is deployed at a remote position and the possibility of unstable signals exists, the equipment reported every t hours (0 < t < 12) can complete one reporting within 3t hours, and is considered to be online; if the continuous time is 3t hours, the report is not sent, and the report is taken off-line;
(2) Device report data analysis
The problem of reporting data by common equipment in water supply projects mainly comprises the following steps: when offline, no data is reported, no equipment sensor data is reported, and the sensor data is abnormal; wherein sensor data anomalies refer to monitoring data that exceeds a normal range associated with water supply.
In the above technical scheme, in the step S2, the internet of things platform analyzes the online and offline state of the device and the data reported by the device in real time, and generates corresponding alarm information in time according to the abnormal conditions that the device is offline, the device only has heartbeat data and no sensor monitoring data, the device sensor data has jump codes and error data, and the fault is conveniently located and traced; for the periodically reported equipment monitoring data, the same fault of the same equipment can periodically generate a plurality of repeated alarm information for filtering; when the reported data is judged by the system and is identified as the continuous occurrence of the same event, only the alarm of the initial event is reserved until the event is recovered; the same event that occurs again after recovery is determined to be a new event, and a new alarm is generated.
In the above technical solution, in the step S3, a large number of chained alarm events caused by a certain source fault are called event tides, and in an event tides, the events are layered and distributed in a tree-like causal sequence, and mainly consist of three parts: the system comprises a root event, a subordinate event caused by the root event and a driving path from the root event to a subordinate event, wherein the path can trace back the occurrence reason of the event forward or backward;
and combing all alarm information and equipment component association relations of the project, establishing a root fault tracing rule base, and positioning the root fault component through a plurality of subordinate alarm events.
In the above technical scheme, in the step S3, if the flow meter and the pressure meter monitoring data of the same monitoring point are transmitted to the internet of things platform through the RTU in the water supply project,
when the alarm prompts that the flow and pressure monitoring data of the monitoring point are abnormal at the same time, the root event may be an RTU fault;
when the alarm prompts that the flow monitoring data is abnormal and the pressure is normal, the root event may be a flowmeter fault;
when the alarm prompts that the pressure monitoring data is abnormal and the flow is normal, the root event may be a pressure gauge fault.
In the above technical solution, in the step S4, a fault tracing knowledge base is established to store all fault events, fault reasons, monitoring data conditions before faults and association relations among the fault components currently known by each component of the front-end sensing device, where the fault events include: antenna damage, communication module damage, battery aging, battery unable charging, RTU software fault, the fault of monitoring data that the reason of sensor trouble led to, no data report, have heartbeat data and sensor data, monitoring data unusual shake, jump code, error data, voltage and signal are normal before the trouble.
In the above technical solution, in the step S5,
the reasons in the fault tracing knowledge base are analyzed, so that the battery problem is often the main reason in the fault caused by the self reason of the equipment, and therefore, the analysis of the battery state before the fault is considered to determine whether the battery problem is the battery problem;
based on historical monitoring data before the failure of the alarm component is analyzed by the Mann-Kendall algorithm, aiming at voltage data, the voltage of a normal battery is stable and accompanied by small random fluctuation, when the alarm component is failed due to battery problems, the voltage of the battery before the failure can wholly show a descending trend, and the Mann-Kendall algorithm is adopted for carrying out overall trend analysis on the voltage:
defining a voltage test statistic S:；
where sign is a sign function whenWhen the ratio is less than, equal to or greater than 0,sign（X i -X j ）taking-1, 0 and 1;
；
and acquiring voltage data of the equipment before the fault for calculation, analyzing whether a battery of the equipment before the fault has a problem, and when a Z calculation result is smaller than 0, considering that the voltage is in a general descending trend, considering that the battery of the equipment has the problem, and causing the fault.
The water supply project equipment fault tracing method based on the Internet of things platform has the following advantages: (1) The invention can find out the fault type in time, and overcomes the problems of dependence on manual experience and hysteresis in treatment in the prior art; (2) By filtering the alarm event, a plurality of unnecessary alarms can be reduced, and the availability of the alarms is improved; (3) And the fault judgment is preferentially carried out on the battery, the tracing range is reduced, and the tracing accuracy is effectively improved.
Drawings
Fig. 1 is a flow chart of a water supply project equipment fault tracing method based on an internet of things platform.
Detailed Description
The following detailed description of the invention is, therefore, not to be taken in a limiting sense, but is made merely by way of example. While at the same time becoming clearer and more readily understood by way of illustration of the advantages of the present invention.
Referring to fig. 1, it can be seen that: the invention provides a water supply project equipment fault tracing method based on an internet of things platform, which comprises the following steps of:
s1: the method comprises the steps that an equipment data analysis system is established by an Internet of things platform, and states of all equipment and reported data are analyzed in real time;
the internet of things platform of the water supply project is connected with a large number of front-end sensing devices such as flow, pressure, water quality and water level, and besides completing device data connection, a device data analysis system is required to be established, and whether the offline and online states of the devices and the reported data of the devices are normally analyzed in real time or not is determined.
(1) On-line and off-line status analysis of devices
Reporting frequency of monitoring equipment of a water supply project is generally reported for a plurality of times every day, and considering that some monitoring equipment is deployed at a remote position and the possibility of unstable signals exists, the equipment reported every t hours (0 < t < 12) can complete one reporting within 3t hours, and is considered to be online; if the continuous time is 3t hours, the report is not sent, and the report is taken off-line;
(2) Device report data analysis
The problem of reporting data by common equipment in water supply projects mainly comprises the following steps: when offline, no data is reported, no equipment sensor data is reported, and the sensor data is abnormal; the abnormal sensor data refers to monitoring data exceeding the normal range related to water supply, such as pipe network flow pressure exceeding the upper limit of pipeline specification, water tank water level exceeding the depth of the water tank, and obvious error data such as water level, pressure, water quality reporting negative number, etc.
S2: generating an alarm aiming at equipment which is offline and has heartbeat data and no sensor data, and carrying out alarm filtering at the same time;
the internet of things platform analyzes the online and offline state of the equipment and the data reported by the equipment in real time, and aims at the abnormal conditions that the equipment is offline, the equipment only has heartbeat data and no sensor monitoring data, the equipment sensor data has jump codes, error data and the like, corresponding alarm information needs to be generated in time, so that the fault is positioned and traced; for the periodically reported equipment monitoring data, the same fault of the same equipment can periodically generate a plurality of repeated alarm information, and filtering is needed; when the reported data is judged by the system and is identified as the continuous occurrence of the same event, only the alarm of the initial event is reserved until the event is recovered; the same event that occurs again after recovery is determined to be a new event, and a new alarm is generated.
By filtering the alarm event, a lot of unnecessary alarms can be reduced, and the availability of the alarms is improved.
S3: establishing a root cause fault tracing rule base, analyzing an alarm event and positioning an alarm component;
a large number of cascading alarm events caused by a certain root cause fault are called event tide, in one event tide, the events are layered and distributed in a tree-shaped causal sequence, and mainly comprise three parts: the root event, the subordinate event caused by the root event and a driving path from the root event to a subordinate event can trace back the occurrence reason of the event forward or backward.
The association relation between all alarm information and equipment parts of the project is combed, a root fault tracing rule base is established, and the root fault parts can be positioned through a plurality of subordinate alarm events; for example, in a water supply project, the flow meter and pressure meter monitoring data of the same monitoring point are transmitted to an internet of things platform through an RTU, and when an alarm prompts that the flow and pressure monitoring data of the monitoring point are abnormal at the same time, a root event can be an RTU fault; when the alarm prompts that the flow monitoring data is abnormal and the pressure is normal, the root event may be a flow meter fault.
S4: establishing a fault tracing knowledge base for storing all fault reasons currently known by each component of the front-end sensing equipment;
the fault tracing knowledge base is established to store all fault events, fault reasons, pre-fault monitoring data conditions, association relations among fault parts and the like which are known at present for all parts of the front-end sensing equipment, such as faults of monitoring data caused by antenna damage, communication module damage, battery aging, battery incapability of charging, RTU software faults, sensor faults and the like, no data report, heartbeat data, no sensor data, abnormal jitter of the monitoring data, jump codes, error data, pre-fault voltage, whether signals are normal or not and the like.
S5: analyzing historical monitoring data before the failure of the alarm component based on a Mann-Kendall algorithm, and combining a failure tracing knowledge base to locate a failure cause;
different external reasons or equipment self reasons are likely to cause the same fault phenomenon, if fault tracing matching is performed only by collecting the currently known fault reasons of all components, a situation that more tracing results are matched by one fault phenomenon is easily formed, and the fault tracing accuracy cannot be effectively improved. Through analyzing and finding the reasons in the fault tracing knowledge base, the battery problem is often the main reason in the fault caused by the self reasons of the equipment, so that the analysis of the battery state before the fault is considered to determine whether the battery state is the battery problem, the fault tracing range can be effectively reduced, and the tracing precision is improved.
The voltage of the normal battery can be in a relatively stable state, and the voltage of the normal battery is accompanied by small random fluctuation, so that the problem battery shows a descending trend, and the descending process is accompanied by random fluctuation of the voltage, so that the voltage is always reduced until the power supply requirement of equipment cannot be met, and data abnormality or even no data report is caused.
Based on the Mann-Kendall algorithm, historical monitoring data before the failure of the alarm component is analyzed, and mainly aiming at voltage data, the voltage of a normal battery is stable and accompanied by small random fluctuation, when the alarm component is failed due to battery problems, the voltage of the battery before the failure can wholly show a descending trend, and the Mann-Kendall algorithm is adopted to analyze the trend of the voltage data before the failure for a period of time:
defining a voltage test statistic S:；
where sign is a function of the sign,nfor the length of the pre-fault voltage sequence,X i 、X j respectively corresponding voltage monitoring values of the ith and the j time sequences whenWhen the ratio is less than, equal to or greater than 0,sign（X i -X j ）taking-1, 0 and 1; voltage trend value；
And acquiring voltage data of the equipment before the fault for calculation, analyzing whether the battery of the equipment before the fault has a problem, and when the calculation result of the voltage trend value Z is smaller than 0, considering that the voltage is in a general descending trend, considering that the battery of the equipment has a problem, and causing the fault.
Therefore, after the fault component is positioned in the step S3, the historical voltage data of the component before the fault is analyzed by combining the knowledge base established in the step S4, and particularly whether the fault is caused by the battery fault of the equipment is judged, so that the tracing range is further reduced, specific fault reasons are positioned, and the tracing accuracy is effectively improved.
Case:
the internet of things platform prompts that a certain monitoring station is in offline and has no data report, the alarm is filtered, the equipment is offline continuously, and the alarm is not repeatedly generated. Because no new data is reported all the time, the alarm is not released all the time, equipment faults need to be traced, an alarm event is analyzed according to a root fault tracing rule base established by projects, and the positioning fault component can be an RTU by combining the phenomenon that neither the sensor nor the heartbeat data is reported.
Based on a fault tracing knowledge base established by the project, historical data such as flow, pressure and voltage before the fault are analyzed, the flow and the pressure data before the fault are found to be in a normal range, and the voltage data is in a descending trend wholly before the fault for a period of time, so that the fault is located according to the fault tracing knowledge base, the equipment offline and the data which are caused by the fault of the battery cannot be reported, then operation and maintenance personnel conduct manual fault investigation to ensure the problem of the battery, the data is recovered after the battery is replaced, and the fault tracing technology is applied and verified on the project.
Other not described in detail belong to the prior art.
Claims (7)
1. The water supply project equipment fault tracing method based on the Internet of things platform is characterized by comprising the following steps of:
s1: the method comprises the steps that an Internet of things platform is accessed to water supply project equipment data, a data analysis system is established, and online and offline states of all the equipment and reported monitoring data are analyzed in real time;
s2: generating an alarm aiming at equipment which is offline, has heartbeat data, has no sensor data and has abnormal sensor data, and filtering the alarm;
s3: establishing a root-cause fault tracing rule base, analyzing alarm events, and positioning a root-cause fault component through a plurality of subordinate alarm events;
s4: establishing a fault tracing knowledge base for storing all fault reasons currently known by each component of the front-end sensing equipment;
s5: analyzing historical monitoring data before the failure of the alarm component based on a Mann-Kendall algorithm, and positioning specific failure reasons by combining algorithm analysis with a failure tracing knowledge base;
in the step S5 of the process described above,
the reasons in the fault tracing knowledge base are analyzed, so that the battery problem is often the main reason in the fault caused by the self reason of the equipment, and therefore, the analysis of the battery state before the fault is considered to determine whether the battery problem is the battery problem;
based on the Mann-Kendall algorithm, historical monitoring data before the failure of the alarm component is analyzed, and aiming at voltage data, the voltage of a normal battery is stable and accompanied by small random fluctuation, when the alarm component is failed due to battery problems, the voltage of the battery before the failure can wholly show a descending trend, and the Mann-Kendall algorithm is adopted to carry out trend analysis on the voltage data before the failure for a period of time:
defining a voltage test statistic S:；
where sign is a function of the sign,nfor the length of the pre-fault voltage sequence,X i 、X j respectively corresponding voltage monitoring values of the ith and the j time sequences whenWhen the ratio is less than, equal to or greater than 0,sign（X i -X j ）taking-1, 0 and 1; voltage trend value；
And acquiring voltage data of the equipment before the fault for calculation, analyzing whether a battery of the equipment before the fault has a problem, and when a Z calculation result is smaller than 0, considering that the voltage is in a general descending trend, considering that the battery of the equipment has the problem, and causing the fault.
2. The method for tracing the faults of the water supply project equipment based on the internet of things platform according to claim 1 is characterized in that in the step S1, the internet of things platform of the water supply project is connected with a large number of front-end sensing equipment of flow, pressure, water quality and water level, and besides completing equipment data connection, an equipment data analysis system is also required to be established, and real-time analysis is carried out on the offline and online states of the equipment and whether the reported data of the equipment are normal or not.
3. The method for tracing the faults of the water supply project equipment based on the platform of the internet of things according to claim 1 or 2, wherein the step S1 comprises the following steps:
(1) On-line and off-line status analysis of devices
Reporting frequency of monitoring equipment of a water supply project is generally reported for a plurality of times every day, and considering that some monitoring equipment is deployed at a remote position and the possibility of unstable signals exists, the equipment reported every t hours (0 < t < 12) can complete one reporting within 3t hours, and is considered to be online; if the continuous time is 3t hours, the report is not sent, and the report is taken off-line;
(2) Device report data analysis
The problem of reporting data by common equipment in water supply projects mainly comprises the following steps: when offline, no data is reported, no equipment sensor data is reported, and the sensor data is abnormal; wherein sensor data anomalies refer to monitoring data that exceeds a normal range associated with water supply.
4. The method for tracing the faults of the water supply project equipment based on the Internet of things platform according to claim 1 is characterized in that in the step S2, the Internet of things platform analyzes the online and offline state of the equipment and the data reported by the equipment in real time, and corresponding alarm information is generated in time according to the abnormal conditions that the equipment is offline, the equipment has only heartbeat data and no sensor monitoring data, the equipment sensor data has jump codes and error data, so that the faults are positioned and traced; for the periodically reported equipment monitoring data, the same fault of the same equipment can periodically generate a plurality of repeated alarm information for filtering; when the reported data is judged by the system and is identified as the continuous occurrence of the same event, only the alarm of the initial event is reserved until the event is recovered; the same event that occurs again after recovery is determined to be a new event, and a new alarm is generated.
5. The method for tracing faults of water supply project equipment based on the platform of the internet of things according to claim 1, wherein in the step S3, a large number of chained alarm events caused by a certain root fault are called event tides, and in one event tides, the events are layered and distributed in a tree-shaped causal sequence and mainly consist of three parts: the system comprises a root event, a subordinate event caused by the root event and a driving path from the root event to a subordinate event, wherein the path can trace back the occurrence reason of the event forward or backward;
and combing all alarm information and equipment component association relations of the project, establishing a root fault tracing rule base, and positioning the root fault component through a plurality of subordinate alarm events.
6. The method for tracing the faults of the water supply project equipment based on the platform of the Internet of things according to claim 5, wherein in the step S3, if the flow meter and the pressure meter monitoring data of the same monitoring point are transmitted to the platform of the Internet of things through the RTU,
when the alarm prompts that the flow and pressure monitoring data of the monitoring point are abnormal at the same time, the root event may be an RTU fault;
when the alarm prompts that the flow monitoring data is abnormal and the pressure is normal, the root event may be a flowmeter fault;
when the alarm prompts that the pressure monitoring data is abnormal and the flow is normal, the root event may be a pressure gauge fault.
7. The method for tracing the faults of the water supply project equipment based on the platform of the internet of things according to claim 1, wherein in the step S4, a fault tracing knowledge base is established for storing all fault events, fault reasons, monitoring data conditions before faults and association relations among fault components currently known by each component of the front-end sensing equipment, and the fault events comprise: antenna damage, communication module damage, battery aging, battery unable charging, RTU software fault, the fault of monitoring data that the reason of sensor trouble led to, no data report, have heartbeat data and sensor data, monitoring data unusual shake, jump code, error data, voltage and signal are normal before the trouble.
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