DACH: Domain Adaptation Without Domain Information

Causal Disentanglement: A Generalized Bearing Fault Diagnostic Framework in Continuous Degradation Mode

In recent years, the identification of out-of-distribution faults has become a hot topic in the field of intelligent diagnosis. Existing researches usually adopt domain adaptation methods to complete the generalization of diagnostic knowledge with the aid of target domain data, but the acquisition of fault samples in real industries is extremely time-consuming and costly. Moreover, most researches focus on samples with fixed fault levels, ignoring the fact that system degradation is a continuous process. In response to the above intractable problems, this article proposed a causal disentanglement network (CDN) to realize cross-machine knowledge generalization and continuous degradation mode diagnosis. In CDN, multitask instance normalization and batch normalization structure was proposed to learn task-specific knowledge and enhance the informativeness of the extracted features. On this basis, a causal disentanglement loss was proposed, which minimized the mutual information of features between subtask structures and captured the causal invariant fault information for better generalization. The experimental results proved the superiority and generalization ability of CDN, and the visualization results proved the performance of CDN in causality mining.

Whitening-Net: A Generalized Network to Diagnose the Faults Among Different Machines and Conditions

Intelligent bearing diagnostic methods are developing rapidly, but they are difficult to implement due to the lack of real industrial data. A feasible way to deal with this problem is to train a network through laboratory data to mine the causality of bearing faults. This means that the constructed network can handle domain deviations caused by the change of machines, working conditions, noise, and so on which is, however, not a simple task. In response to this problem, a new domain generalization framework-Whitening-Net-was proposed in this article. This framework first defined the homologous compound domain signal as the data basis. Subsequently, the causal loss was proposed to impose regularization constraints on the network, which enhances the network's ability to mine causality. To avoid domain-specific information from interfering with causal mining, a whitening structure was proposed to whiten the domain, prompting the network to pay more attention to the causality of the signal rather than the domain noise. The results of diagnosis and interpretation proved the ability of Whitening-Net in mining causal mechanisms, which shows that the proposed network can generalize to different machines, even if the tested working conditions and bearing types are completely different from the training domains.

Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adaptivity to high dynamics and aging equipment, they are usually hungry for massive labels, mostly contributed by human engineers at a high cost. Fortunately, domain adaptation enhances the model generalization by utilizing the labeled source data and the unlabeled target data. However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, since they assume that the conditional distributions are equal. This assumption works well in the static data but is inapplicable for the time series data. Even the first-order Markov dependence assumption requires the dependence between any two consecutive time steps. In this article, we assume that the causal mechanism is invariant and present our Causal Mechanism Transfer Network (CMTN) for time series domain adaptation. By capturing causal mechanisms of time series data, CMTN allows the data-driven models to exploit existing data and labels from similar systems, such that the resulting model on a new system is highly reliable even with limited data. We report our empirical results and lessons learned from two real-world case studies, on chiller plant energy optimization and boiler fault detection, which outperform the existing state-of-the-art method.

Discriminative Sparse Filtering for Multi-source Image Classification

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.