As typical and complex mechatronic system, health state of the wind turbine (WT) is of significant importance to the sustained and reliable service. However, it is noted that influenced by the seasonal or fitful wind, WTs unavoidably serve in the dynamically varying environment. In this event, most of the currently available indicators expose deficiency in regard of the false or missed alarms due to the coupled condition interference. To address this issue and improve the reliability of the mechatronic system, a novel statistic discrepancy oriented cyclo-non-stationary (CNS) indicator is developed in this article. First, characteristics of the recorded degradation samples are revealed by a multiparametric model, during which the consistency is verified and improved by the hypothesis test. Second, a specific speed-dependent slicing (SDS) operator is then designed, aiming to alleviate the varying-speed-induced modulation interference at the different degradation stages. With this developed SDS operator, a CNS indicator, which can well adapt to the dynamically varying environment during the operating process, is subsequently developed by incorporating the resampling-based statistic discrepancy evaluating mechanism. Experiments indicate that the proposed method can effectively characterize the health state of the transmission parts of the industrial WT under varying speed conditions.

Recently, feature mode decomposition (FMD) has been proposed and has demonstrated robust performance in the application of rotating machinery fault diagnosis. However, FMD does not have parameter adaptability, and its influencing parameters (i.e., segment number, mode number and filter length) need to be defined in advance. Inspired by the decomposition mode of wavelet packet transform, this article cleverly proposes a binary decomposition structure of FMD to form the concept of FMD packet (FMDP). Furthermore, the tiling structure of FMDP is constructed, thus forming the FMDPgram. In FMDPgram, the frequency band of the raw signal is evenly divided into 2 segments, and each FMD generates two submode components (SMCs), thus simplifying the hyperparameter optimization into a single-parameter optimization. Additionally, an indicator called narrowband peak-to-average ratio (NPAR) is proposed for optimal SMC positioning, and which is displayed in the FMDPgram tile map. First, performing FMDPgram with NPAR on the raw signal. Then, the optimal SMC is located based on the NPAR in the FMDPgram tile map. Finally, the envelope power spectrum of the optimal SMC is calculated to characterize the fault characteristic information. FMDPgram is tested on simulated and experimental data, and compared with FMD, spectral kurtosis, VMD and improved Kurtogram to evaluate its performance in rolling bearing diagnosis under low signal-to-noise ratio and non-Gaussian interference.

In previous studies, the VMDPgram was creatively proposed by combining variational mode decomposition (VMD) with wavelet packet transform (WPT). Although the VMDPgram demonstrates excellent performance in bearing fault diagnosis, there are still some issues that need to be further studied. In light of this, this work conducts the in-depth studies of VMDPgram for the unresolved issues. First, in view of the obvious second-order cyclostationarity of vibration signal of rotating machinery such as bearing, especially in the presence of localized faults, the unbiased autocorrelation (AC) function is introduced. Here, the kurtosis value of the unbiased AC of the squared envelope of each sub-intrinsic modal function (sub-IMF) within the constrained range is calculated, generating the new method named AutoVMDPgram. Second, the modified adaptive resonance bandwidth (MARB) is introduced to constrain the decomposition depth of the AutoVMDPgram. Third, the cumulative evaluation index based on the unbiased AC kurtosis of the square envelope of the sub-IMF is proposed as a measure to locate the optimal sub-IMF without determining whether the resonant frequency range is divided into different sub-IMFs. AutoVMDPgram is tested on simulated and experimental data and compared with Autogram, spectral kurtosis (SKs), and VMD to evaluate its performance in rolling bearing diagnostics.

Neural networks have been widely applied in the field of bearing fault diagnosis. However, many existing studies focus on bearings with constant rotational speeds, and there is a lack of research on neural networks for diagnosing faults in bearings with varying speeds. In practice, bearings are always working under varying rotational speeds. This paper proposes a one-dimensional and two-dimensional hybrid neural network combined with wavelet transform for bearings fault diagnosis under variable speeds. Instead of using one-dimensional convolutional kernels, wavelets are employed as generating a two-dimensional feature map that can represent the relationship between signal time and frequency, and the two-dimensional convolutional layer extracts features from the output of the one-dimensional convolutional layer, which is the time-frequency representation obtained through wavelet transformation of the signal. The feasibility of the proposed method is validated on a publicly available dataset from the University of Ottawa.

Condition monitoring of assets is significant to the efficiency and reliability of industrial automation systems. However, the accuracy of condition monitoring results is easily impaired by variational environments and volatile operations, especially for complex automation systems. In this article, an environmentally adaptive and contrastive representation learning method is proposed to address the problem. To suppress the unexpected effects of environmental variations on operating data, a regression model between the operational and environmental variables is developed. The variable regression adjustment is achieved by solving a penalized optimization problem based on spline functions, and the solution is explicitly derived. Then, negative samples and pseudo labels are generated based on the designed pattern of data augmentation, and valid data representations for asset condition monitoring can be obtained by contrastive learning. Moreover, the reference statue of healthy assets is established by kernel density estimation, and control charts are employed for online monitoring with alarm thresholds. Taking wind turbine blades as examples, the remarkable performance of the developed method is demonstrated with real-world measurements from wind farms. Furthermore, comparative analysis with benchmark approaches and ablation study are conducted to reveal the superiority and effectiveness of the proposed method.

The signal-processing and intelligent diagnostic and monitoring methods based on motor current signature analysis for induction motors (IM) usually depend on preset parameters. Moreover, many of them have difficulty in achieving ideal health monitoring effect with strong noise interference and switching working conditions. To overcome these limitations, a novel digital twin architecture called the Ramanujan digital twin (RDT) is composed. This architecture uses the Ramanujan periodic transform as its computational core to detect the potential fault signatures in each monitoring frame. The quantity of interest from IM will be selected and calibrated based on the Bayesian-updated driven calibration mechanism to construct the phenomenal simulation signals with high fidelity to the potential fault signatures. These signals will provide guidance information. The effectiveness and robustness of the RDT are validated through experimental cases.

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a challenging task for which the target fault severities do not exist in the training samples, this article proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods.