Intelligent fault diagnosis methods have gained much attention in industry. An important premise of these methods is that the training and test data maintain the same set of fault classes, known as the closed-set hypothesis, which, however, cannot be guaranteed in fault diagnosis tasks. This can result in potentially unknown faults being incorrectly randomly classified as a known fault class. To overcome this problem, we introduce open-set recognition and proposed an Open Fault Semantic subspace-based open-set Fault Diagnosis and Inference framework (OFS-FDI), which identifies unknown faults while completing the diagnosis of a known class of faults, and furthermore, inferring the possible fault type of unknown samples. First, a fault semantic subspace construction method is proposed to transform the original signal into a set of low-dimensional representation subsets conforming to a conditional Gaussian distribution, which is related to the fault semantic. Then, an outlier score is proposed to determine whether a sample is from an unknown class. Finally, a class directional index (CDI) metric is proposed to perform inferential analysis of possible fault classes. In the experiments based on two typical rotating machines, the outlier detection accuracy of OFS-FDI is improved by up to 0.04% compared to the comparative methods. The accuracy for unknown fault inference is up to 99.65%.

The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA’s lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method.