Power abnormal failure data analyzing device and diagnosing method

Abstract

The invention relates to a power abnormal failure data analyzing device and a diagnosing method, and belongs to the field of equipment failure detection. The analyzing device comprises a signal acquisition unit, a rotary capacitor filter circuit, a signal processor, a power supply module, a communication module and a host computer. The method for diagnosing abnormal failure data by using the device comprises: 1, by using a method of phase space reconstruction, evaluating embedding delay t and optimal embedding dimension d; 2, modeling the acquired data to obtain a neural network predicting initial model; 3, predicting a next output x[i+1]<->; 4, updating the weight of the next predicted BP network; and 5, judging the error. The device and the method have the advantages that the BP neural network is skillfully combined with the phase space reconstruction method, and the device and the method are suitable for various acquisition signal types, have accurate and rapid processing, are applicable to various complex storage working stations, and can effectively analyze and process signal failure, in particular fine failure data. A rotary filter capacitor has strong noise inhibiting capability.

Description

Technical field: [0001] The invention belongs to the field of equipment fault detection, and in particular relates to a data analysis device and a diagnosis method for abnormal electric power faults. Background technique: [0002] At present, the device for analyzing abnormal voltage fault data, if the signal frequency band and the noise frequency band overlap, it is impossible to solve the problem with simple filtering technology, and the noise suppression ability is poor, and the abnormal signal can be detected, but the pulse wave on both sides If it is too high, it will affect the detection of abnormal signals. At present, the abnormal fault diagnosis methods include fuzzy logic method, wavelet method, pattern recognition method, etc. These methods mainly use statistical methods to analyze the data, and these methods assume that the dynamic characteristics of the object are random , leading to unsatisfactory diagnostic accuracy, powerless for small signal faults, in addit...

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Problems solved by technology

[0002] At present, for the device that analyzes abnormal voltage fault data, if the signal frequency band overlaps with the noise frequency band, it is impossible to solve the problem with simple filtering technology. The suppression ability is poor, and the abnormal signal can be detected, but the pulse wave on both sides is too high, which affects the detection of abnormal signal. At present, the abnormal fault diagnosis methods include fuzzy logic method, wavelet method, pattern recognition method, etc. These methods mainly use statistical methods To analyze the data, these methods assume that the dynamic characteristics of the object are random, resulting in unsatisfactory diagnostic accuracy and powerlessness for small signal faults. In addition, the wavelet fault diagnosis method has the problem of high false alarm rate, which is seriously limited. The application of the wavelet fault diagnosis method in the field of practice, the fuzzy logic method does not have the ability of self-learning, there is a bottleneck of difficulty in obtaining fuzzy diagnostic knowledge, especially the fuzzy relationship between faults and symptoms is difficult to determine, it is easy to cause missed reports and misdiagnosis, and The optimal selection of fuzzy rules, membership functions and decision-making algorithms is difficult, while the pattern recognition method has the difficulty of expressing and determining the fault feature vector and discriminant function, and it is powerless for the diagnosis of novel faults. Insufficient

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