A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions - Yuejian Chen; Xuemei Liu; Wenkun Fan; Ningyuan Duan; Kai Zhou (Machines, 2024)
The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual.