CN111653291B - Intelligent health monitoring method for power equipment based on voiceprint - Google Patents
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Abstract
The invention relates to the technical field of electric power, and provides an intelligent health monitoring method for electric power equipment based on voiceprints, which comprises the following steps: s1, laying a voiceprint signal collector on the site of the power equipment, and uniformly deploying a plurality of wireless sensors on each power equipment to collect voiceprint signals of each equipment of the power equipment working on the site; s2, the voiceprint signal collector consists of a Bluetooth wireless communication module, a data packet forwarding module, a 4G communication module and a power module; s3, the cloud voiceprint data memory is composed of an IP communication module, a data storage module and a data processing module; and S4, periodically performing feature extraction, fault diagnosis and threshold correction on the voiceprint data stored in the disk by adopting the equipment health intelligent manager. The invention solves the problems of difficult deployment of acquisition nodes, unstable work of a data server, low fault diagnosis speed and precision, nonadjustable fault diagnosis threshold value and the like in the conventional power equipment health monitoring technology.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to an intelligent health monitoring method for electric power equipment based on voiceprints.
Background
With the expansion of the investment scale of a power grid, the rapid development of the new energy power generation industry and the rapid reformation of an urban grid rural power grid, the normal operation of power equipment is a necessary premise for ensuring the normal operation of the whole power supply system and ensuring good power supply. However, power equipment is generally in a charged state and cannot be detected in a conventional mode, indirect judgment is mostly performed by acquiring equipment temperature data in the prior art, but the judgment is greatly influenced by the environment, the judgment accuracy is not high, and the reference significance is not large.
In the process of live operation of the power equipment, the power equipment can generate specific sound and vibration which can represent the state of the equipment, the sound is unique to the equipment, and the sound can be measured and analyzed through an electroacoustic instrument, so that the sound is called as the sound print and vibration which are carried by the sound and represent the operating state of the power equipment. By utilizing the characteristic, the abnormal detection is carried out on the detection voiceprint information of the detected equipment, so that the working condition of the equipment can be prejudged, the preknown and the elimination are realized before the equipment breaks down, and the loss caused by abnormal power grid outage due to sudden failure of the power equipment is avoided.
For example, chinese patent documents: CN201811612080.0 a fault detection method, device and system for electromechanical devices, and discloses a fault detection method for electromechanical devices, wherein because the sound of electromechanical devices in a normal operating state is generally different from the sound of electromechanical devices in a fault, it is only necessary to place a sound collection device in the surrounding area of electromechanical devices to collect the sound of electromechanical devices, and then determine whether electromechanical devices are in fault by extracting the voiceprint features of the collected sound and verifying the extracted voiceprint features by using a voiceprint library. A contact type sensor does not need to be installed on the electromechanical equipment, the physical characteristics of the electromechanical equipment do not interfere with the sound acquisition equipment, the limitation of fault detection on the electromechanical equipment is small, the fault detection precision of the electromechanical equipment is improved, and accidents in the area where the electromechanical equipment is located are avoided. However, the fault diagnosis is based on the existing data in the voiceprint library for verification, fault false alarm can be generated when the acquired voiceprint data is abnormal, the fault false alarm cannot be corrected when the fault false alarm occurs, meanwhile, fault diagnosis false alarm can be generated when the voiceprint data is abnormal along with the aging of equipment operation, environmental changes and the like, the monitoring effect is not ideal, and the equipment cannot be effectively and healthily monitored.
For another example, CN201910885692.5 discloses a method for identifying faults of rotating mechanical equipment based on voiceprint signals, which includes the following steps: establishing a screen spectrum database; collecting voiceprint signals of the rotating mechanical equipment through an audio collecting assembly; converting the electric signal corresponding to the voiceprint signal into a digital signal through an audio conversion component; judging whether the rotating mechanical equipment has faults or not by comparing the frequency spectrum value of the digital signal with a spectrum screening database; judging whether the rotary mechanical equipment has faults or not through wavelet packet analysis; and comparing the states of the rotating mechanical equipment analyzed by the two modes, if the states are consistent, the rotating mechanical equipment is in a normal state or a fault state, and if the states are inconsistent, analyzing and comparing the states of the rotating mechanical equipment again until the comparison is consistent. Although the method can reduce false alarm of fault diagnosis to a certain extent by adopting two detection and analysis modes, the method only checks the existing database, and fault diagnosis false alarm can be generated when voiceprint data generated along with the aging of equipment operation, environmental change and the like are abnormal, the monitoring effect is not ideal, and the detection cost is increased by arranging the two detection modes at the same time.
Further, as CN201910570185.2 discloses a voiceprint recognition-based online monitoring method and system for power equipment, which collect data streams in at least one preset first sound pickup and at least one preset second sound pickup. Determining that the established connection relation of the first sound pickup including the data stream and the corresponding data stream node, the at least one second sound pickup including the data stream and the corresponding data stream node and the original data of the data server are matched; and outputting a matching result. Compared with the prior art, the method and the system for monitoring the power equipment on line based on voiceprint recognition have the advantages that the running state of the transformer is monitored on line through the sound signals generated when the monitoring equipment runs, the actual running condition of the equipment is mastered from multiple aspects by matching with a transformer substation monitoring system, the management of the transformer substation system is optimized, and the method and the system have important practical significance for improving the maintenance efficiency and reliability of the equipment and prolonging the service life of the equipment. But the detection and analysis are complex and are not suitable for the overall health monitoring of various devices on the site of the power equipment.
Therefore, in the existing voiceprint-based power equipment health monitoring technology, either the deployment of collection nodes is difficult, the work of a data server is unstable, or the detection and analysis are complex and not suitable for simultaneous health monitoring of various equipment, or the fault diagnosis false alarm rate is high and low in precision, and meanwhile, the voiceprint data monitoring threshold value for fault diagnosis is not adjustable, so that various equipment on the site of the power equipment cannot be effectively and uniformly monitored.
Disclosure of Invention
Therefore, aiming at the problems, the invention provides the intelligent health monitoring method for the power equipment based on the voiceprint, which is used for uniformly monitoring the health of various devices on the site of the power equipment, is convenient for deploying the acquisition nodes, is stable in work of the data server, is high in fault diagnosis speed and precision, and can adjust the fault diagnosis threshold.
In order to solve the technical problem, the invention adopts the following scheme: a voiceprint-based intelligent health monitoring method for electrical equipment comprises the following steps:
the method comprises the following steps that S1, a voiceprint signal collector is arranged on a power equipment field, a plurality of wireless sensors are uniformly arranged on each power equipment to collect voiceprint signals of each equipment of the power equipment working on the field, and each wireless sensor collects voiceprint data according to set collection frequency and transmits the voiceprint data to the voiceprint signal collector through wireless Bluetooth;
s2, the voiceprint signal collector consists of a Bluetooth wireless communication module, a data packet forwarding module, a 4G communication module and a power module, wherein the Bluetooth wireless communication module is in communication connection with each wireless sensor for acquiring voiceprint signals, and sends voiceprint signal acquisition commands to each wireless sensor for acquiring voiceprint signals through wireless connection and receives voiceprint signal acquisition data acquired by the wireless sensors for acquiring voiceprint signals; the data packet forwarding module packages voiceprint signal acquisition data received by each wireless sensor for acquiring voiceprint signals into a data packet, generates an integrity abstract by adopting an SHA algorithm, and then sends the data packet and the integrity abstract to a cloud voiceprint data memory through the 4G communication module;
s3, the cloud voiceprint data storage is composed of an IP communication module, a data storage module and a data processing module, wherein the IP communication module receives a data packet and integrity abstract voiceprint data sent by a voiceprint signal collector through the Internet; the data processing module carries out data packet integrity check by using the integrity abstract of the voiceprint data and an SHA algorithm and transmits the data packet passing the integrity check to the data storage module; the data storage module stores the data packet which passes the integrity check in a disk in a file format;
s4, periodically extracting characteristics, diagnosing faults and correcting threshold values of voiceprint data stored in the disk by adopting an intelligent health manager, wherein the intelligent health manager consists of a data reading module, a characteristic extraction module, a fault diagnosis module, a fault early warning module and a threshold value correction module, and the data reading module reads time-domain voiceprint data from a voiceprint data file stored on the disk periodically; the method comprises the steps that a characteristic extraction module extracts voiceprint data to calculate a time domain average energy value and a frequency domain average energy value, the time domain average energy value calculation method of the voiceprint data is that each data value in the time domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the time domain average energy value of the voiceprint data, the frequency domain average energy value calculation method of the voiceprint data is that time domain voiceprint data is firstly subjected to Fourier transform to obtain frequency domain voiceprint data, then each data value in the frequency domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the frequency domain average energy value of the voiceprint data; the fault diagnosis module compares the time domain average energy value and the frequency domain average energy value obtained by calculation with a characteristic threshold value to carry out fault diagnosis, and when the time domain average energy value and the frequency domain average energy value are both greater than the current characteristic threshold value, the equipment is diagnosed as being in a fault state; the fault early warning module sends the fault state information detected by the fault diagnosis module, the corresponding time domain average energy value, the corresponding frequency domain average energy value and the characteristic threshold value of the equipment to a field power engineer; and a threshold correction module of the equipment health intelligent manager receives the threshold correction command and the recommended correction value of the equipment sent by the field power engineer and sets the recommended correction value as a new characteristic threshold of the equipment.
Further, in the step S1, the wireless sensor is a wireless MEMS vibration sensor, the wireless MEMS vibration sensor is powered by a lithium secondary battery, and the wireless MEMS vibration sensor is integrated with a Bluetooth wireless interface to perform high-safety wireless data transmission; the wireless MEMS vibration sensor shell is made of high-strength engineering plastics.
Furthermore, in the step S1, when a field work voiceprint signal is acquired for a single large power device, a plurality of wireless sensors are deployed on the large power device in an array manner to acquire voiceprint data.
By adopting the technical scheme, the invention has the beneficial effects that: the voiceprint signal data collected by the plurality of wireless sensors are collected and forwarded to the cloud voiceprint data storage through the voiceprint signal collector, deployment cost of voiceprint data collection is saved, the voiceprint data storage is used for receiving the voiceprint data sent by the voiceprint signal collector and carrying out check storage, working requirements of a data storage server are hardly met due to the fact that the working environment of the equipment is severe, and the situation that the data storage server cannot work stably is avoided; the method comprises the steps that a field power engineer confirms and processes received fault early warning, when the field power engineer finds that a fault is mistakenly reported, a threshold correction command and a suggested correction value can be sent to an intelligent health manager of the equipment, a threshold correction module of the intelligent health manager of the equipment receives the threshold correction command and the suggested correction value sent by the field power engineer of the equipment and sets the suggested correction value as a new characteristic threshold of the equipment, and therefore the defects of high conventional static characteristic threshold and false alarm rate are overcome.
Detailed Description
The invention will now be further described with reference to specific embodiments.
The invention discloses a voiceprint-based intelligent health monitoring method for power equipment, which comprises the following steps of:
the method comprises the following steps that S1, a voiceprint signal collector is arranged on a power equipment field, a plurality of wireless sensors are uniformly deployed on each power equipment to collect voiceprint signals of each equipment of the power equipment working on the field, each wireless sensor collects voiceprint data according to set collection frequency and transmits the voiceprint data to the voiceprint signal collector through wireless Bluetooth, wherein each wireless sensor is a wireless MEMS vibration sensor, the wireless MEMS vibration sensor is powered by a lithium-ion battery, and the wireless MEMS vibration sensor is integrated with a Bluetooth wireless interface to perform high-safety wireless data transmission; the shell of the wireless MEMS vibration sensor is made of high-strength engineering plastics, and a plurality of wireless sensors are deployed on a large power device in an array mode to acquire voiceprint data when the voiceprint signals of the single large power device are acquired on site by the power device;
s2, the voiceprint signal collector consists of a Bluetooth wireless communication module, a data packet forwarding module, a 4G communication module and a power module, wherein the Bluetooth wireless communication module is in communication connection with each wireless sensor for acquiring voiceprint signals, and sends voiceprint signal acquisition commands to each wireless sensor for acquiring voiceprint signals through wireless connection and receives voiceprint signal acquisition data acquired by the wireless sensors for acquiring voiceprint signals; the data packet forwarding module packages voiceprint signal acquisition data received by each wireless sensor for acquiring voiceprint signals into a data packet, generates an integrity abstract by adopting an SHA algorithm, and then sends the data packet and the integrity abstract to a cloud voiceprint data memory through the 4G communication module;
s3, the cloud voiceprint data storage is composed of an IP communication module, a data storage module and a data processing module, wherein the IP communication module receives data packets and integrity abstract voiceprint data sent by a voiceprint signal collector through the Internet; the data processing module carries out data packet integrity check by using the integrity abstract of the voiceprint data and an SHA algorithm and transmits the data packet passing the integrity check to the data storage module; the data storage module stores the data packet which passes the integrity check in a disk in a file format;
s4, periodically performing feature extraction, fault diagnosis and threshold correction on voiceprint data stored in the disk by adopting an equipment health intelligent manager, wherein the equipment health intelligent manager consists of a data reading module, a feature extraction module, a fault diagnosis module, a fault early warning module and a threshold correction module, and the data reading module periodically reads time-domain voiceprint data from a voiceprint data file stored on the disk; the method comprises the steps that a characteristic extraction module extracts voiceprint data to calculate a time domain average energy value and a frequency domain average energy value, the time domain average energy value calculation method of the voiceprint data is that each data value in the time domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the time domain average energy value of the voiceprint data, the frequency domain average energy value calculation method of the voiceprint data is that time domain voiceprint data is firstly subjected to Fourier transform to obtain frequency domain voiceprint data, then each data value in the frequency domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the frequency domain average energy value of the voiceprint data; the fault diagnosis module compares the time domain average energy value and the frequency domain average energy value obtained by calculation with a characteristic threshold value to carry out fault diagnosis, and when the time domain average energy value and the frequency domain average energy value are both greater than the current characteristic threshold value, the equipment is diagnosed as being in a fault state; the fault early warning module sends the fault state information detected by the fault diagnosis module, the corresponding time domain average energy value, the corresponding frequency domain average energy value and the characteristic threshold value of the equipment to a field power engineer; and a threshold correction module of the intelligent device health manager receives the threshold correction command and the recommended correction value of the device sent by the field power engineer and sets the recommended correction value as a new characteristic threshold of the device.
According to the invention, for small or medium-sized equipment, whether an array type plurality of wireless sensors need to be arranged to collect voiceprint data of the equipment or not can be seen on the site of the power equipment according to the circumstances, and the collection of the time domain voiceprint data is to sample the voiceprint data according to the set cycle time, namely to collect the time domain voiceprint data in equal duration within the set interval time period.
The voiceprint signal data collected by a plurality of wireless sensors are collected and forwarded to a cloud voiceprint data storage through a voiceprint signal collector, deployment cost of voiceprint data collection is saved, the voiceprint data storage is used for receiving the voiceprint data sent by the voiceprint signal collector and carrying out check storage, working requirements of a data storage server are hardly met due to the fact that the working site environment of the equipment is severe, the data storage server cannot work stably, the voiceprint data storage is stored through the cloud voiceprint data storage, the cloud voiceprint data storage is often configured in a machine room with the working environment meeting the requirements, the server of the voiceprint data storage is stable and safe in working, the voiceprint data stored in a magnetic disk of the cloud voiceprint data storage is periodically subjected to feature extraction, fault diagnosis and threshold correction through an equipment health intelligent manager, the cloud voiceprint data storage is compared with a threshold value through the equipment health intelligent manager, whether the equipment is diagnosed in advance or not, and when the equipment is diagnosed to have a fault, a fault early warning module sends fault state information detected by a fault diagnosis module, a corresponding average energy value and a frequency domain characteristic threshold value of the voiceprint data of the equipment to an engineer; the method comprises the steps that a field power engineer confirms and processes received fault early warning, when the field power engineer finds fault false alarm, a threshold correction command and an advised correction value which are sent to an intelligent device health manager can be received by a threshold correction module of the intelligent device health manager, the threshold correction command and the advised correction value which are sent by the field power engineer are received by the threshold correction module of the intelligent device health manager, and the advised correction value is set to be a new characteristic threshold of the device, so that the defects of high false alarm rate and traditional static characteristic thresholds are overcome, the whole intelligent health monitoring method is convenient to deploy collection nodes, a data server is stable in work, high in fault diagnosis speed and precision, and capable of automatically adjusting the fault diagnosis threshold, improving the reliability of device health monitoring and diagnosis, capable of uniformly monitoring various devices on the site of the power device in a healthy mode, and capable of being widely popularized and applied.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A voiceprint-based intelligent health monitoring method for power equipment is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps that S1, a voiceprint signal collector is arranged on a power equipment field, a plurality of wireless sensors are uniformly arranged on each power equipment to collect voiceprint signals of each equipment of the power equipment working on the field, and each wireless sensor collects voiceprint data according to set collection frequency and transmits the voiceprint data to the voiceprint signal collector through wireless Bluetooth;
s2, the voiceprint signal collector consists of a Bluetooth wireless communication module, a data packet forwarding module, a 4G communication module and a power module, wherein the Bluetooth wireless communication module is in communication connection with each wireless sensor for acquiring voiceprint signals, and sends voiceprint signal acquisition commands to each wireless sensor for acquiring voiceprint signals through wireless connection and receives voiceprint signal acquisition data acquired by the wireless sensors for acquiring voiceprint signals; the data packet forwarding module packages voiceprint signal acquisition data received by each wireless sensor for acquiring voiceprint signals into a data packet, generates an integrity abstract by adopting an SHA algorithm, and then sends the data packet and the integrity abstract to a cloud voiceprint data memory through the 4G communication module;
s3, the cloud voiceprint data storage is composed of an IP communication module, a data storage module and a data processing module, wherein the IP communication module receives a data packet and integrity abstract voiceprint data sent by a voiceprint signal collector through the Internet; the data processing module carries out data packet integrity check by using the integrity abstract of the voiceprint data and an SHA algorithm and transmits the data packet passing the integrity check to the data storage module; the data storage module stores the data packet which passes the integrity check into a disk in a file format;
s4, periodically extracting characteristics, diagnosing faults and correcting threshold values of voiceprint data stored in the disk by adopting an intelligent health manager, wherein the intelligent health manager consists of a data reading module, a characteristic extraction module, a fault diagnosis module, a fault early warning module and a threshold value correction module, and the data reading module reads time-domain voiceprint data from a voiceprint data file stored on the disk periodically; the method comprises the steps that a characteristic extraction module extracts voiceprint data to calculate a time domain average energy value and a frequency domain average energy value, the time domain average energy value calculation method of the voiceprint data is that each data value in the time domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the time domain average energy value of the voiceprint data, the frequency domain average energy value calculation method of the voiceprint data is that time domain voiceprint data is firstly subjected to Fourier transform to obtain frequency domain voiceprint data, then each data value in the frequency domain voiceprint data is respectively squared, then accumulated and summed, and finally averaged to calculate the frequency domain average energy value of the voiceprint data; the fault diagnosis module compares the time domain average energy value and the frequency domain average energy value obtained by calculation with a characteristic threshold value to diagnose the fault, and when the time domain average energy value and the frequency domain average energy value are both greater than the current characteristic threshold value, the equipment is diagnosed to be in a fault state; the fault early warning module sends the fault state information detected by the fault diagnosis module, the corresponding time domain average energy value, the corresponding frequency domain average energy value and the characteristic threshold value of the equipment to a field power engineer; and a threshold correction module of the equipment health intelligent manager receives the threshold correction command and the suggested correction value of the equipment sent by the field power engineer and sets the suggested correction value as a new characteristic threshold of the equipment.
2. The intelligent health monitoring method for voiceprint-based power equipment according to claim 1, wherein the method comprises the following steps: in the step S1, the wireless sensor is a wireless MEMS vibration sensor, the wireless MEMS vibration sensor is powered by a lithium subcell, and the wireless MEMS vibration sensor is integrated with a Bluetooth wireless interface to perform high-safety wireless data transmission; the wireless MEMS vibration sensor shell is made of high-strength engineering plastics.
3. The intelligent health monitoring method for power equipment based on voiceprint according to claim 1 or 2, wherein: in the step S1, when the field work voiceprint signal acquisition is carried out on a single large-scale power device, a plurality of wireless sensors are deployed on the large-scale power device in an array mode to acquire voiceprint data.
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| CN112649502A (en) * | 2020-11-18 | 2021-04-13 | 华北电力大学 | System and method for monitoring abnormal state based on voiceprint diagnosis |
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| US9892744B1 (en) * | 2017-02-13 | 2018-02-13 | International Business Machines Corporation | Acoustics based anomaly detection in machine rooms |
| CN106898346A (en) * | 2017-04-19 | 2017-06-27 | 杭州派尼澳电子科技有限公司 | A kind of freeway tunnel safety monitoring system |
| CN110782622A (en) * | 2018-07-25 | 2020-02-11 | 杭州海康威视数字技术股份有限公司 | Safety monitoring system, safety detection method, safety detection device and electronic equipment |
| CN109658954A (en) * | 2018-12-27 | 2019-04-19 | 广州势必可赢网络科技有限公司 | A kind of fault detection method for electromechanical equipment, apparatus and system |
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