Mechanical fault diagnosis under varying speed conditions has gradually become an important issue in rotating machinery monitoring, especially the research on compound fault diagnosis is still rare. Order analysis is one of the most effective and widely used methods to eliminate the effects of speed fluctuations, however it is difficult to extract compound fault features directly. Moreover, there are often many restrictions on the number and locations of sensors to be installed in practice, which also brings great challenges to compound fault diagnosis under varying speed conditions. In this paper, a novel compound fault detection method with virtual multi-channel signals in angle domain is proposed, to solve these problems for rolling bearing monitoring under varying speed conditions. First, we aim to use only a vibration acceleration sensor and a non-contact rotational speed sensor to adapt to limitations on sensor installation in practical applications. However, this may result in an underdetermined problem when trying to detect compound fault with only a single-channel vibration signal. Then we make it up through constructing virtual multi-channel signals based on variational mode decomposition theory, before which the non-stationary vibration signal is converted from time domain into a stationary signal in angle domain with computed order tracking to eliminate speed fluctuations. Finally, these virtual signals can be easily taken as input to a normal independent component analysis, and different faults can be detected in the order spectrum of each independent component. Case studies on rolling bearing experiments verified the advantages of the proposed method in compound fault detection under varying speed conditions.

The research of intelligent fault diagnosis method has made great progress. The prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant labeled data, which is difficult to satisfy in practice. Fortunately, we can obtain a large amount of rolling bearing failure data under laboratory conditions. Inspired by the idea of transfer learning, we propose a deep dynamic adaptive transfer network (DDATN) for intelligent fault diagnosis of rolling bearings. In addition to performing transfer diagnosis under different working conditions and failure degrees of the same type of bearing, it is also able to accomplish the task of cross-machine fault diagnosis from bearings under laboratory conditions to the bearings in practical applications. In the DDATN, the marginal probability distribution and conditional probability distribution of the data are aligned by dynamic domain adaptation using weight factor. First, the original vibration signal of the bearing is first processed to establish the source and target domains. Then, pseudo-label learning on target domain unlabeled data is performed and the transferable features between domains are extracted through the deep parameter-shared neural networks. Next, by performing dynamic adaptation on the extracted transferable features and optimizing the intelligent fault diagnosis model through backpropagation, the complete transfer diagnosis task in the target domain is accomplished. The effectiveness of the proposed DDATN method is demonstrated through variable working conditions and cross-machine transfer fault diagnosis tasks. Compared with other intelligent fault diagnosis methods, the proposed method shows clear advantages.

In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method.

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.

Sufficient data is necessary for intelligent fault diagnostic approaches. However, in practice, it is often the case that only limited fault data is available due to various reasons, making it a challenge to accurately identify the health condition of bearings. To deal with the limited fault data issue, data augmentation strategies, such as generative adversarial network (GAN), are widely utilized. However, GAN has the disadvantages of being difficult to train and restricted ability to generate new data when the fault sample size is limited. Specifically, GAN requires a long training time and abundant training data to make the distribution of generated data closer to the distribution of actual data. This article presents a novel data augmentation approach with compressed sensing for fault diagnosis of bearings to better address the issue of limited fault data. The generated data by compressed sensing is diverse. In addition, the generated data is highly similar to the original data in frequency domain, thus retaining the main feature information of the original data. Furthermore, data augmentation achieved through compressed sensing requires less training data and has lower computational complexity. For bearing fault diagnosis under limited failure data, the limited fault data is first augmented based on compressed sensing, allowing for high fidelity reconstruction and high diversity data generation. Then, the augmented data is utilized to train a deep convolutional neural network to automatically learn and extract features for fault identification. The effectiveness of the presented approach is verified using two bearing datasets.