The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

The labeled monitoring data collected from the electromechanical system is limited in the real industries, traditional intelligent fault diagnosis methods cannot achieve satisfactory accurately diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semi-supervised graph convolution deep belief network (SSGCDBN) algorithm is proposed in this study. Specifically, the labeled and unlabeled samples are firstly employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss (CL). Finally, the local and discriminative objective functions are reconstructed under the semi-supervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66% accuracy with only 10% of training labeled data, which indicates that it is a promising semi-supervised intelligent fault diagnosis method.

Nowadays, transfer learning and domain adaptation are widely used in prognostics and health management of rotating machinery, greatly broadening its applications in scenarios with multiple operating conditions. However, almost all existing prognostic methods employ a single-source domain adaptation (SDA), ignoring the domain-shift within the source domain, and fail to make full use of multi-source historical data. To overcome these drawbacks, a multisource domain adaptation network (MDAN) for regression tasks is proposed in this article. MDAN learns domain-invariant features and supervision from multiple sources and provides better generalization in the target domain than traditional SDA methods. Besides, a new MDAN-based framework is presented for remaining useful life prediction of bearings under multiple operating conditions. Specifically, we input the time-frequency representation of the vibration signals into MDAN to achieve end-to-end training and inference across operating conditions. Case studies on the run-to-failure datasets validate the effectiveness of proposed method, and the comparison shows that it outperforms the state-of-the-art methods.

The rotor-bearing system is widely used in various high-end electro-hydraulic equipment, which provides specific support, rotation, and other integral functions. However, the fluctuating working conditions of the rotor-bearing system will cause more significant disordered fluctuations in the measured signals. This article proposes a new algorithm called multiscale deep graph convolutional networks (MS-DGCNs) to alleviate this problem. The designed MS-DGCNs algorithm combines a new multiscale intra-class fine coarse-grained processing and multiscale graph convolution kernels. Accordingly, an intelligent fault diagnosis method based on MS-DGCNs for the rotor-bearing system under fluctuating conditions is designed to learn more feature representations and accuracy. First, a sliding window is employed to divide the collected vibration signals into a series of subsignals. The multiscale signal processing is performed to obtain different degrees of the fine-coarse time series. Then, a graph convolution with the multiscale convolution kernel is designed. Finally, the soft-max classifier is combined for intelligent fault diagnosis. The experimental results of the double-span rotor-bearing system under fluctuating conditions well demonstrate that the method has the higher accuracy and generalization.