With the development of artificial intelligence technology, data-driven fault diagnostics and prognostics in industrial systems have been a hot research area since the large volume of industrial data is being collected from the industrial process. However, imbalanced distributions exist pervasively between faulty and normal samples, which leads to imprecise fault diagnostics and prognostics. In this paper, an effective imbalance learning algorithm Easy-SMT is proposed. Easy-SMT is an integrated ensemble-based method, which comprises synthetic minority oversampling technique (SMOTE)-based oversampling policy to augment minority faulty classes and EasyEnsemble to transfer an imbalanced class learning problem into an ensemble-based balanced learning subproblem. We validate the feasibility and effectiveness of the proposed method in a real wind turbine failure forecast challenge, and our solution has won the third place among hundreds of teams. Moreover, we also evaluate the method on prognostics and health management 2015 challenge datasets, and the results show that the model could also achieve good performance on multiclass imbalance learning task compared with baseline classifiers.

Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.

In modern industrial engineered systems, variant working conditions disturb the distributions of machines’ operational data, which results in different feature distributions (DFD) problems for fault prognostics. Domain adaptation (DA) have been proved good at dealing DFD problems, and several deep DA-based methods have been also proposed in fault prognostics filed. However, existing methods refer to the DA tasks from one working condition to another, without considerations of transferring between datasets under complex working conditions. The prior distribution of working conditions will influence the distributions of machines’ operational data, and few studies take prior distribution of working conditions into consideration of DA for fault prognostics. Thus, in this paper, a working-condition-based deep domain adaptation network (Deep wcDAN) is proposed to overcome the DFD problems caused by variant complex working conditions. In the proposed method, CNNs combines LSTMs with domain adaptive transfer technique to minimize the distribution discrepancy between training and testing datasets. Furthermore, a working-condition-based MMD (wcMMD) is proposed to optimize the DA process based on the prior distribution of each working condition. The performance of proposed model is evaluated and the negative transfer effects have been analyzed based on C-MAPSS datasets. The results show that the proposed method performs better than baseline methods on predicting remaining useful life (RUL) with DFD problems.