An ensemble deep learning approach for untrained compound fault diagnosis in bearings under unstable conditions

Abstract Based on the dimension invariance property of the data-driven bearing fault diagnosis method, unstable condition data can result in the loss of information and reduced diagnostic accuracy due to inconsistent data dimensions. Furthermore, the fixed parameters of the output layer restrict its ability to accurately diagnose faults beyond the training set, particularly compound faults with limited data. To address these challenges, this study proposes an ensemble deep learning approach for identifying untrained compound faults in bearings operating under non-stationary conditions. Firstly, a signal angular domain processing technique is employed to standardize the dimensionality of the bearing’s state information, effectively mitigating information loss. Secondly, a feature extraction model is established to dynamically capture local microscopic and multilevel features utilizing the adaptability of convolutional neural network (CNN), and it can mine the relevant features of compound faults through the single-fault features. In the verification process, the kmeans algorithm with scalable classification is used to optimize the classifier of CNN. Specifically, the number of cluster centers in kmeans is set to exceed the count of training fault categories. Identification of untrained compound faults is achieved by calculating the Euclidean distances between each feature and the cluster centers, based on the principle of minimum distance. It addresses the challenge of inadequate diagnostic rates for untrained compound faults. The diagnostic outcomes prove that the proposed method has a high diagnostic robustness and generalization ability, which can effectively solve the problem of insufficient fault data and provide a new way of diagnosing untrained compound faults.