Alternating current field measurement (ACFM) is widely used in the quantitative detection of crack due to its advantages of noncontact measurement and high accuracy. However, the noncontact measurement introduces signal interference including constant and random lift-off. Both lift-offs bring challenges to the accurate quantification of cracks. It is difficult to obtain bothBxandBzsignals effectively. In this article, a new 1D-AE-PINN framework to accurately quantify the crack under the lift-off interference is proposed. A novel insight feature ofBxsignal with physical information about the crack size is studied and integrated into loss functions of the 1D-AE-PINN. The features encoded by 1D-AE-PINN are used as input to the quantization network. The advantages of 1D-AE-PINN in accuracy are proved by comparative experiments. The results show that the length and depth of the crack can be measured by onlyBxsignal. The mean squared errors of length and depth are 0.66 and 0.39 mm2.

End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited samples is challenging in industrial practice. The existing limited samples methods usually focus on the data distribution or learning strategy with particularity. Generative adversarial network (GAN) provides a data generation solution with portability in fault diagnosis with limited samples. However, GAN has problems with gradient vanishing, weak extraction of global features, and redundant training. This article proposes a dual-threshold attention-guided GAN (DTAGAN) to generate high-quality infrared thermal (IRT) images to assist fault diagnosis. First, Wasserstein distance and gradient penalty are combined to design loss function to avoid gradient vanishing. Second, attention-guided GAN is constructed to extract global thermal-correlation features of IRT images. Finally, dual-threshold training mechanism is developed to improve the generation quality and training efficiency. The comparative experiments show that DTAGAN is superior to comparison methods in fault diagnosis of rotor-bearing system under speed fluctuation and limited samples.

Due to the high price of the product and the limitation of laboratory conditions, reliability tests often get a small number of failed samples. If the data are not handled properly, the reliability evaluation results will incur grave errors. In order to solve this problem, this work proposes an artificial intelligence (AI) enhanced reliability assessment methodology by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms. First, a single hidden layer BNN model is constructed by fusing small samples and prior information to obtain the 95% confidence interval (CI) of the posterior distribution. Then, the DE algorithm is used to iteratively generate optimal virtual samples based on the 95% CI and small samples trends. A reliability assessment model is reconstructed based on double hidden layers BNN model by combining virtual samples and test samples in the last stage. In order to verify the effectiveness of the proposed method, an accelerated life test (ALT) of the subsurface electronic control unit (S-ECU) was carried out. The verification test results show that the proposed method can accurately evaluate the reliability life of a product. And compared with the two existing methods, the results show that this method can effectively improve the accuracy of the reliability assessment of a test product.

In closed-loop feedback control systems, faults are propagated through the feedback link, which eventually leads to the abnormality of the entire system. Generally, it is very difficult to identify the faults of systems under the influence of the closed-loop feedback link. The existence of redundancy improves the reliability of the system. Meanwhile, it also poses new challenges to the fault diagnosis of multiple redundant systems. In this regard, a causality-based method is proposed for the fault diagnosis of closed-loop feedback control system with multiple modular redundancy. The dynamic Bayesian networks for fault diagnosis are established based on sensor data and system parameters. The networks consist of four layers, which are sensors, performances, monitors, and faults, respectively. Furthermore, the conditional probabilities of the fault nodes are calculated by Noisy-OR and Noisy-MAX models. The proposed method can dynamically evaluate system performance and integrate other monitoring information as evidence to assist faults diagnosis and location. A double modular redundant control system for a subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. The influence of sampling frequency, noise, and redundancy mode on diagnosis results is studied and discussed in the case study.

Difficult-to-cut materials, such as superalloys and titanium alloys, have been increasingly used in aerospace and other fields. However, commonly used mechanical milling methods for difficult-to-cut materials have problems, including low processing efficiency and severe tool wear. Electrical discharge assisted arc milling (EDAAM) is a novel high-efficiency machining method proposed by the authors to solve these processing problems. The machining efficiency achieved by EDAAM when processing difficult-to-cut materials is much higher than that achieved by mechanical milling and conventional electrical discharge machining (EDM) milling. However, if EDAAM adopts the traditional EDM servo control method, it will easily form deep and large discharge craters on the surface of the workpiece, which may lead to high machining errors and cause the workpiece to be scrapped. To ensure normal processing, low-energy discharge parameters that may reduce the processing efficiency have to be adopted. To address the above issues and achieve the optimal machining condition of difficult-to-cut materials, mathematical models and system of intelligent servo for EDAAM are proposed. An EDAAM discharge information dataset is obtained, and the intelligent recognition and classification method for discharge states is proposed. The EDAAM intelligent servo control system is proposed based on mathematical models created. The experimental results of superalloy Inconel 718 by EDAAM show that, compared with the nonintelligent servo control system, the intelligent servo control system can increase the material removal rate by up to ten times, reduce the relative electrode wear rate by 37%, and avoid the scrapping of the workpiece.

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results.

Vibration signals and infrared images have different advantages and characteristics. Although a few recent researches have explored their information fusion in rotating machinery fault diagnosis, they show limited performance when facing strong interference and imbalanced cases. Therefore, a fusion framework based on confidence weight support matrix machine (CWSMM) is proposed. In this framework, CWSMM can not only fully leverage the structure information of infrared thermography images and vibration time–frequency images, but also has the following novelties. First, CWSMM uses dynamic penalty factors for different class samples to address the class imbalance problem. Second, by using the prior knowledge of matrix samples, a confidence weight assignment strategy is designed for CWSMM to improve the robustness. Last, the Dempster–Shafer (D-S) evidence theory is applied to fuse the posterior probability outputs of CWSMMs using different measurements. Experiment results demonstrate that the proposed method has promising fault diagnosis performance, specifically under strong interference and imbalanced datasets.

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature extraction, and training a deep learning model requires large samples and a long time. In addition, vibration monitoring has the shortcomings of contact measurement, small coverage, and noise interference. To address these problems, this article proposes a new fault diagnosis method with the least square interactive support matrix machine (LSISMM) and infrared thermal images. In this method, a novel matrix-form classifier called LSISMM is constructed under the concept of nonparallel interactive hyperplanes to fully leverage the structure information of infrared thermal images. To improve the computation efficiency, a new least square loss constraint is designed for LSISMM. Besides, we derive an effective solution framework based on the alternating direction method of the multiplier (ADMM) framework. The constructed LSISMM is directly used to analyze the collected thermal images of rotating machinery under time-varying speeds. Experiment results demonstrate that the proposed method is superior to state-of-the-art methods in terms of diagnosis accuracy and efficiency, especially under small thermal image samples.

Preventive maintenance (PM) activities in battery energy storage systems (BESSs) aim to achieve a better status in long-term operation. In this article, we develop a reinforcement learning-based PM method for the optimal PM management of BESSs equipped with prognostics and health management capabilities. A multilevel PM framework is established to generate a PM action strategy considering costs, capacity, and reliability simultaneously. Finite costs are the constraints, and reliability is the objective according to capacity degradation, respectively. The proposed PM agent with an integrated Monte Carlo tree search and a deep neural network (DNN) utilizes the state-of-health information of large-scale batteries in the BESS and selects optimal maintenance actions. The DNN is used as the state-action value function to extend the ability to address PM problems with large state-action spaces. The case of a BESS with 9 × 12 × 4 batteries in a fleet is simulated via Python. The results show that the PM agent can achieve efficient and steady decision-making proficiency in BESS PM management.

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.