Ensuring reliable machine operation is dependent on effective fault detection. Feature indicators construction is considered as the foundation for operational condition assessment among various detection methods. However, the nuclear circulating water pump (NCWP) experiences severe disturbances due to noise and changes in operating conditions. Additionally, the complex movement of the gears causes signal interference, which may result in the feature indicators failing. To address the above issues, this paper proposes a generalized features construction method (GFC) to realize unsupervised planetary gearbox fault detection. First, a feature relevance evaluation method is presented to filter intrinsic mode functions (IMFs) obtained through ensemble empirical mode decomposition (EEMD) for noise reduction. Second, feature indicators are constructed based on interpretable frequency information. Then, global and local features are evaluated respectively to realize fault detection and further detection of faults from the sun or planetary gears. Finally, the effectiveness of the proposed method is demonstrated through data obtained from the NCWP test bench.

Domain Adaptation can transfer crossdomain diagnosis knowledge by minimizing the divergence of labeled source and unlabeled target data. However, the model neglects to maximize physics prior knowledge during feature extraction and distribution alignment, resulting in a non-interpretable model even negative transfer. Hence, a physics-informed domain adaptation network, termed Adaptive Fault attention Residual Network (AFARN), is proposed. First, an adaptive fault attention mechanism is designed to refine features guided by bearing fault characteristics, suited to generating diagnosis-relevant features. Then, several metrics are applied to minimize the marginal and conditional distribution discrepancy of features, thus generalizing the model from source to target domain. The AFARN utilizes the fault characteristics and label information simultaneously to train the model, which can enhance the distribution alignment of diagnosis-relevant features, thus providing an interpretable knowledge transfer. Finally, experiments on public and circulating water pump datasets show that AFARN can enhance fault feature learning and diagnosis accuracy.

Journal bearings are the key components of the nuclear circulating water pump (NCWP), and accurate remaining useful life (RUL) prediction is of great significance for improving the reliability, safety, and maintenance planning of NCWP. However, it is difficult to quantify the uncertainty of bearing RUL based on the current deep learning (DL) model, resulting in a lack of credibility and effective convincing for RUL predicted by the model. Meanwhile, all existing hybrid models are basically simple combinations, and they cannot solve the uncertainty quantification problem of RUL predicted by DL. Hence, the bearing RUL prediction method based on a dynamic interactive hybrid model is proposed. First, a degradation model based on a nonlinear enhanced generalized Wiener process (EGWP) is proposed, which combines gated neural networks and time-varying drift coefficients to describe the nonlinear degradation process of bearing. Then, a corrective gated recurrent unit (CGRU) network is designed to learn and predict real-time degradation increments, and the parameters of the degradation model are dynamically updated through the history and prediction of degradation increments. Finally, the bearing RUL prediction is given by the CGRU network, and the probability density function of RUL is given by the proposed hybrid model. The performance of the proposed method is evaluated using the PHM 2012 bearing dataset and the NCWP journal bearing dataset. The results show that our proposed method can effectively predict bearing RUL and its uncertainty.

Real-time and accurate prediction of remaining useful life (RUL) is important to safe operation and maintenance (O&M) planning of mechanical equipment. However, the uncertainty of online RUL prediction is difficult to predict with most current deep learning (DL)-based methods, making the prediction results difficult to convince. Furthermore, the offline-trained DL model is unable to adaptively update the network parameters online when acquiring new data, leading to a decrease in RUL prediction accuracy. To overcome these problems, an innovative approach based on the incremental contrast hybrid model is proposed for online RUL prediction with uncertainty quantification, which combines the contrastive learning transformer (CLformer) with the enhanced generalized Wiener process (EGWP) to describe trends in mechanical degradation. First, a CLformer is developed for online trend prediction, and an incremental contrastive learning strategy is designed for online adaptive updating of CLformer parameters to reduce prediction offset errors. Then, the degradation increments within the EGWP state-space equations are predicted online by the proposed CLformer network for online updating of EGWP parameters. Finally, online prediction of the machine RUL is provided by the CLformer, whereas the hybrid model provides the probability density function of RUL. The effectiveness of the proposed method is verified using two publicly available datasets and the journal-bearing dataset of the nuclear-circulating water pump. The results demonstrate the ability of the proposed method to dynamically update model parameters when new data are acquired online while giving the RUL prediction values and uncertainties.

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient samples and ideal operating environments. However, in practice, it is hard to acquire adequate fault samples from high-reliability equipment, thus inducing challenges in applying DL for intelligent diagnosis. Moreover, data distribution shift occurs due to working load variation or environment noise interference, severely degrading DL models’ performance. Aiming at the above problems, this paper proposes a method called Q networks calibrated ensemble (QCE). Specifically, two improved deep Q networks are employed to enhance the attention to critical feature samples and fault samples by quantifying feature distribution and utilizing fault prior knowledge. Afterward, the calibrated Choquet integral is adopted to achieve the models’ ensemble, further enhancing the generalization ability and robustness of the diagnosis results. The proposed method has been verified on the NCWP test bench. Results indicate the superiority and reliability of the QCE method. Besides, results when working load changes demonstrate the outstanding generalization ability of the proposed method for cross-domain fault diagnosis with class imbalance. Furthermore, Grad-CAM visualization verifies that the proposed method effectively learns fault features instead of noise.

Due to coupled nonlinearities and complex measurement noise, assess the condition of the rotor system remains a challenge, particularly in cases where historical run-to-failure data is lacking. To this end, we proposed a hierarchical physics-informed neural network (HPINN) to identify/discover the ordinary differential equations (ODEs) of a healthy/faulty rotor system from noise measurements and then assess the rotor condition based on the discovered ODEs. Specifically, the ODEs of a healthy rotor system are first stably identified from noisy measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODEs, the extra fault terms in the ODEs of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN, in which the phase compensation and alternating training strategy are developed to guarantee training convergence. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified with simulation and test bench datasets, showing the potential for practical industrial applications. Note to Practitioners —This paper investigates the health assessment problem (condition monitoring and fault diagnosis) of the rotor system, a critical component in large rotating machinery. The proposed HPINN provides a hierarchical framework to firstly identify the ODEs of healthy rotor system and then discover the ODEs of faulty rotor system with limited monitoring data (3-5 seconds data collected from sensor commonly, depending on the rotating speeds). With the mathematical terms of discovered fault, the fault can be diagnosed and a health indicator (HI) can be constructed to assess the condition of rotor system in a fully interpretative way. This approach is applicable to large rotating machinery in safety-critical industries, such as circulating water pumps.