Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early and accurate battery system fault detection to realize rapid early warning. The method first adopts the support vector data description model mapping the feature of unlabeled voltage and temperature into a minimum volume hypersphere in high-dimensional space. When the feature is located outside the hypersphere, it is judged to be faulty. Then, to overcome the problem of hyperparameters selection, Bayesian optimization and a small amount of label data are used to iteratively train the model. This step can greatly improve the fault detection ability of the model, which is conducive to mining early and minor faults. Finally, the proposed model is compared with three unsupervised fault detection models, principal component analysis, kernel principal component analysis, and support vector data description to validate the performance of fault detection and robustness, respectively. The experimental results show that: 1. the proposed model has high detection accuracy in all four fault datasets, especially in the highly concealed cumulative short-circuit fault, which is substantially ahead of the other three models; and 2. The proposed model has higher and more stable accuracy than the other three models even in the case of a large range of signal-to-noise ratio.

Estimation of the sintering state has importance for clinker quality improvements and the safe operation of the rotary kiln. Class imbalanced thermal signals usually pose challenges in feature extraction and abnormal state recognition. In this paper, a novel framework that integrates prior knowledge and hidden information is developed for sintering state recognition in the class imbalance condition. For discriminative feature extraction of imbalanced data, a cascaded stack autoencoder (SAE) model is proposed to fuse our prior knowledge and hidden information. The model includes a feature extraction SAE and a deep fusion SAE: the former extracts hidden information from thermal signals, and the latter deeply fuses and compresses our prior knowledge and hidden information. For the class imbalance of sintering samples, we propose a data-dependent kernel modification optimal margin distribution machine (ddKMODM) as a sintering state recognition model. Modifying the original kernel function by a conformal function depending on the data distribution in kernel space, ddKMODM can change the local volume expansion coefficient of the feature space to eliminate the negative effects caused by imbalanced samples. Experiments on real data show that the proposed framework can balance the detection rate of each state in the class imbalanced condition, and its overall sintering state recognition accuracy exceeds 92%.