Yantai Jereh Oilfield Services Group Co., Ltd.

Xinmeng Wang

Zongwen Wang

Tao Li

Minghua Sun

Hailong Li

A valve fault detection method includes: constructing a valve fault indicator system reflecting characteristics of vibration signals of valve states; establishing an LSTM-AE model by using an LSTM deep model and according to an autoencoder principle, and setting a fault discrimination threshold; inputting training data into the LSTM-AE model based on the valve fault indicator system, to train the LSTM-AE model; and inputting detection data of a to-be-detected valve into the trained LSTM-AE model based on the valve fault indicator system, and comparing an output value obtained by the LSTM-AE model with the set fault discrimination threshold, to judge whether the detected valve is faulty and outputting a first judgment result.

Valve fault detection method and apparatus

A method for generating a portrait of mechanical equipment includes: obtaining all data information related to mechanical equipment; performing data fusion on the data information related to the mechanical equipment to obtain a multi-source data information set; obtaining target attribute data and target state data of the mechanical equipment based on the multi-source data information set; generating attribute labels of the mechanical equipment based on the obtained target attribute data; inputting current target state data of the mechanical equipment into a trained neural network model to obtain current state labels of the mechanical equipment, where the current state labels include state label values of the mechanical equipment; and generating an equipment portrait of the mechanical equipment through attribute label values of the obtained attribute labels and the state label values of the obtained current state labels.

Method for generating portrait of mechanical equipment

The embodiments of the present disclosure provide a fault diagnosis method, a method for building a fault diagnosis model, fault diagnosis equipment, electronic device, and non-transitory computer-readable storage medium. The fault diagnosis method, for diagnosing a fluid device, which includes a suction end and a discharge end, includes: obtaining a data set for diagnosing the fluid device, wherein the data set includes first characteristic data about the suction end, second characteristic data about the discharge end, and input-output difference data, and the input-output difference data represents data difference between the suction end and the discharge end; obtaining a fault diagnosis model; and determining whether the fluid device is in failure based on the fault diagnosis model and the data set.

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Yantai, China

Xinmeng Wang