Incremental learning based fault diagnosis is effective to learn from continuous industrial data on an ongoing basis. However, in the case of non-stationary industrial processes, new data distribution often gradually shifts away from that of historical data, due to equipment aging and manufacturing strategies, etc. Thus, conventional incremental methods with the identical independent distribution (i.i.d) assumption may no longer promise satisfied diagnosis performance. This paper concerns the conditional drift phenomenon, a relaxation of the i.i.d assumption, in which the conditional distribution of industrial data changes within different time. From a mathematical point of view, we firstly give the problem formulation of conditional drift and introduce a target mapping strategy for drift adaptation, under the minimum risk criteria. Then, following this strategy, an incremental diagnosis model with adaptation ability is designed. Particularly, a transformation matrix keeps matching the distributions of historical and new data. Thus, our method can quickly adapt to the conditional drift and be more robust against the evolving environment. The proposed method is applied for diagnosing faults in two industrial processes to demonstrate its effectiveness.

The scarcity of fault samples is a common scenario in the field of fault diagnosis. In the context of mechanical fault diagnosis, the emergence of new working conditions and fault modes renders the availability of samples of target (unseen) faults for model training unfeasible, thus limiting the performance of data-driven methods. Consequently, zero-sample learning and diagnosis of mechanical faults is a challenging task. In this regard, this article proposes a multiattribute learning model, inspired by the zero-shot learning paradigm, for zero-sample mechanical fault diagnosis. The key lies in the shared multiclass attribute classifiers. During the attribute learning process, a convolutional neural network is developed to construct multiclass attribute classifiers, which serve as a mapping between visual features and semantic features. These classifiers are transferred from readily available faults to enhance the capability of diagnosing unseen faults. By minimizing the difference among the fault attributes, the diagnosis of unseen faults is achieved, which includes fault location, size, working load, etc. Experiments on two real datasets verify the efficacy and the superiority of the proposed method.

This study investigates the sample value imbalance problem of process monitoring. A fault detection approach based on variable selection and support vector data description (SVDD) is developed for efficient process monitoring. First, Kullback–Leibler divergence serves as the variable selection algorithm, which highlights the most beneficial information about the concerned faults. The attained variables are segmented by block division to avoid faults information being covered in single space monitoring, so that the relevant variables and the most beneficial information are concentrated in the same block. Then, Kernel principal component analysis is applied in each block to address the challenge that variables may still be high-dimensional and nonlinear. After that, the monitoring result is given based on the proposed SVDD with a restructured radius index, which is more sensitive to the fault. As demonstrated from experimental results on the Tennessee Eastman process, this method is effective and outperforms counterparts with higher mean fault detection rate. Note to Practitioners—Recently, multivariate statistical process monitoring (MSPM) has attracted much attention. In general, MSPM incorporates all variables for the large-scale process. However, only a small number of variables are fault-dependent. Namely, the sample value imbalance problem is encountered in application. In this scenario, the monitoring performance degrades and the online computational complexity increases. To this end, a SVDD-based fault detection method, which considers the fault-related variables, is proposed for process monitoring. The proposed method is verified by the Tennessee Eastman process and it is more sensitive to the concerned fault.