Abnormal detection (AD) plays a crucial role in maintaining system integrity and minimizing risks associated with abnormal behavior or events. Given the practical challenges in obtaining data that covers the entire lifespan of mechanical equipment, we proposed a novel unsupervised AD method with a high sensitivity to early failures. In the proposed time convolutional variational autoencoder - generative adversarial networks (TCVAE-GAN) model, the temporal convolutional network (TCN) module is incorporated into the encoder, decoder and discriminator to improve the reconstruction and feature extraction capabilities of time-series data. Subsequently, the implementation of adversarial training leads to the continuous enhancement of the reconstruction ability of the variational autoencoder and the improvement of the discriminator’s capability to effectively detect early weak faults. Furthermore, utilizing the output of the discriminator directly as a healthy indicator (HI) for AD enhances the interpretability of the results. To verify the accuracy and effectiveness of the proposed method, we conduct a comparison with traditional time-domain methods and five other unsupervised methods. The validation results obtained from both the laboratory datasets and actual wind turbine datasets demonstrate the high sensitivity of our method in detecting early weak faults in bearings.

Operating condition monitoring of wind turbine (WT) key components is of significant importance to preventative maintenance and the improvement of WT reliability. To realize this industrial target, health indicator (HI) construction is a crucial and indispensable step. While most of the recently reported HIs are emphasized effective in stationary cases, they are insufficiently applicable to variable speed conditions. To address this issue, a novel HI through operating speed slicing and discrepancy compensation is proposed in this article for WT generator bearing condition monitoring. First, signal probability distributions of the collected degradation data are appropriately characterized by an optimized multiparameter regression method. Then, benchmark distributions established at the normal state are identified through operating speed slicing, and the discrepancies induced by the time-varying operating condition are subsequently calibrated with a compensation strategy. On this basis, a globally comparable metric, by quantitatively evaluating the degree to which the currently established distribution deviates from the corresponding slice-related benchmark, is accordingly constructed. Experimental tests demonstrate that the proposed HI can make a more effective health state assessment for WT generator bearing under variable speed conditions when compared with the conventional indicators.

This paper proposes an enhanced DMAIC (eDMAIC) method which succeeds from Six Sigma tool for the machining processes in multi-stage, small-batch or one-of-a-kind (M/S/O) production. As a modification and extension of the popular Six Sigma DMAIC, the eDMAIC method also consists of five phases, i.e., define, measure, analyze, improve, and control. Several specific tools in each phase are offered and how to use them is also interpreted. Then, a continuous quality improvement case is illustrated on how the eDMAIC method carries out in a machining workshop. It is proved that eDMAIC method contributes to a systematic solution when the machining processes are unstable. The eDMAIC method also offers an engineering approach to realizing better quality performances with lower cost in the context of M/S/O production with Six Sigma thinking.