Generalized Mapping Fault Diagnosis for Industrial Machine Based on Penalty Gating LSTM

The constant fluctuation of operational conditions in industrial processes results in dimensional changes or corrosion of machine, consequently leading to recurring drift of its property parameters. This phenomenon poses a challenge for diagnosis models capturing fault features of machine by learning mapping patterns with the collected property parameters. To address this issue, a generalized mapping fault diagnosis model (GMFDM) is proposed to diagnose machine faults by using a multimode feature map (MFM) and a penalty gating LSTM (PG-LSTM). First, an MFM is built to obtain the features of faults using the interaction among the fault-related variables. Second, a penalty gating mechanism (PGM) is introduced and incorporated into PG-LSTM. PGM updates the gate units of LSTM based on the results of the MFM to regulate the flow and memory of information, which enables LSTM to dynamically sharpen multiple mapping patterns of faults under the recurring drift. Third, a pattern-aware learning algorithm is designed to update the parameters of PG-LSTM, shaping diverse mapping patterns with a regularization term. Finally, the experimental results show that the GMFDM achieves accuracy of 98.08% in diagnosing the faults of the pump in industrial processes.