Rule engine-based electromechanical equipment fault diagnosis method, system and terminal
A technology for fault diagnosis and electromechanical equipment, which is applied to computer parts, instruments, calculations, etc., can solve the problems of not taking into account the correlation of different data, the deviation of fault diagnosis results, and the single diagnosis result, so as to achieve comprehensive and accurate abnormal diagnosis results , Reduce the waste of network resources and improve the efficiency of fault diagnosis
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Embodiment 1
[0047] Example 1: A rule engine-based fault diagnosis method for electromechanical equipment, such as figure 1 shown, including the following steps:
[0048] S1: Obtain multi-source time series data of the target object, and obtain multiple independent single-source time series data after classifying the multi-source time series data;
[0049] S2: Input the single-source time series data into the corresponding anomaly identification model constructed according to the deep neural network for anomaly identification, and obtain an anomaly category set composed of multiple single-source categories;
[0050] S3: Split the abnormal category set into multiple abnormal subsets, and each abnormal subset includes at least two single-source categories;
[0051] S4: Calculate the estimated fault probability of each abnormal subset according to the correlation of each single source category in the abnormal subset, and arrange the abnormal subsets in descending order according to the estim...
Embodiment 2
[0067] Example 2: A rule engine-based electromechanical equipment fault diagnosis system, such as figure 2 As shown, it includes a data processing module, an anomaly identification module, a class splitting module, an estimation sorting module, a matching diagnosis module and a loop control module.
[0068] The data processing module is used for acquiring multi-source time series data of the target object, and after classifying and processing the multi-source time series data, multiple independent single-source time series data are obtained. The anomaly identification module is used to input the single-source time series data into the corresponding anomaly identification model constructed according to the deep neural network for anomaly identification, and obtain an anomaly category set composed of multiple single-source categories. The category splitting module is used to split the abnormal category set into multiple abnormal subsets, and each abnormal subset includes at lea...
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