In transportation systems, accurately estimating the remaining useful life (RUL) of critical components, such as aircraft engines, Battery Management Systems (BMSs), is crucial for the safe and reliable operation and manufacturing of transportation vehicles. However, most existing research overlooks the underlying physical information, which is vital for more precise RUL prediction. To fill this gap, this paper proposes a physics-informed method for predicting the RUL of key components of transportation vehicles. By integrating the Mamba network with a multi-head attention mechanism, we capture and emphasize key features and trends in the equipment’s operational state, improving prediction accuracy. Additionally, we introduce a Physics-Informed Neural Network (PINN) framework to model the underlying physical relationships between RUL and sensor data, incorporating these relationships as a regularization term in the loss function to enhance predictive capability and interpretability. We conducted experimental validation using the C-MAPSS aircraft engine dataset (operation) and the transportation vehicle chip manufacturing dataset (manufacture). The results show that the proposed method significantly improves the accuracy of RUL prediction, providing strong support for the intelligent maintenance and reliability management of key components in transportation vehicles.

Transfer learning has been widely applied to intelligent fault diagnosis to address the challenge of insufficient labeled data. However, the efficacy of existing semi-supervised domain adaptation (SSDA) methods is often constrained by their critical dependence on pseudo-label quality and the adoption of indiscriminate global alignment strategies. To address the negative transfer induced by these limitations, a deep gated network (DGN) for targeted transfer learning is proposed in this article. First, an anchor-guided manifold clustering (AGMC) method is developed to generate high-quality pseudo-labels by exploiting both the local manifold structure and anchor supervision in the target domain. Subsequently, a feature extractor is constructed to learn discriminative representations from the source domain, integrate high-quality supervisory information from the target domain, and impose constraints on the feature space. Furthermore, a gated domain alignment strategy is designed to achieve precise class-level transfer. This strategy incorporates an integrated gating mechanism to selectively filter out domain-specific features while employing the local maximum mean discrepancy (LMMD) to align the conditional distributions across domains. Finally, the effectiveness and superiority of the proposed method are validated through transfer experiments on both cross-machine bearing and cross-condition two-phase flow datasets.

Process uncertainty, which is usually caused by various factors, is generally subject to unknown complex distribution. However, many existing monitoring methods are established with a single distribution, and thus they may not accurately reflect the uncertainty within process systems. In this study, a probabilistic quality- relevant monitoring (PQM-GMM) is proposed with the Gaussian mixture model to address the aforementioned issue. Different from conventional monitoring methods, the proposed method measures the process uncertainty using multiple Gaussian distributions, which can be used to approximate any unknown complex distribution. Then, the optimization problem of the proposed PQM-GMM model is solved using the expectation maximization (EM) algorithm, which includes an augmented Lagrange multiplier in the M-step for model parameter estimation. Using the obtained results, a quality-relevant monitoring model is established with three statistics. It is noted that the proposed model can also be extended to many existing methods since they share a similar structure. Besides, the detailed information such as initial value selection, missing data problem, computation complexity is discussed. The effectiveness and superiority of the proposed method are tested using a numerical simulation example and a real low-pressure heater application. In comparison with some commonly used quality-relevant methods, the proposed model can be robustly established in the presence of corrupted data, and has a better detection sensitivity for the process anomalies in both process and quality variables. Note to Practitioners—A quality-relevant monitoring method is proposed in this study with Gaussian mixture model (GMM) for detecting the abnormal conditions of industrial processes under harsh environment. Since GMM can be used to approximate any unknown complex distribution, the process uncertainty within the collected data can be meticulously measured using the proposed PQM-GMM model. Besides, the quality-independent faults and quality-related faults can also be effectively distinguished using the designed monitoring statistics.

The historical data collected from industrial processes are generally disturbed by ambient noise and outliers. Hence, accurate estimation of process uncertainty is essential in order to correctly determine the status of the process systems. In this study, a robust probabilistic quality-relevant monitoring model with a Laplace distribution is proposed for industrial process monitoring under noisy environment. Because of the heavy tailed characteristic of Laplace distribution, the proposed model is more robust than models with Gaussian distribution. The solution of the proposed probabilistic model is provided through variational Bayesian inference and maximum likelihood estimation after recasting Laplace distribution as Gaussian scale mixtures. Based on the obtained model parameters and estimated latent variables, a quality-relevant monitoring model can be established and four statistics are designed. According to the calculated statistics, the proposed method can effectively detect and differentiate quality-relevant from quality-independent faults. The performance of the proposed method is illustrated using a numerical simulation and a condenser application, which are disturbed by ambient noise and outliers. Experimental results demonstrate that Laplace distribution can better reveal the process uncertainty to effectively alleviate their negative effect. As a result, the proposed method performs better than some commonly used quality-relevant monitoring strategies.

Due to various reasons, outliers, ambient noise and missing data inevitably exist in the industrial processes, and thus the robustness is important when establishing monitoring models. In this study, a robust dissimilarity analytics model (RDAM) is established with Laplace distribution to detect process anomalies in noisy environment. Because of the heavy-tailed characteristic of Laplace distribution, the proposed RDAM method is more robust to ambient noise and outliers when compared to Gaussian distribution-based models. Besides, the missing data problem is also considered and solved in the model development procedure. Using the variational Bayesian inference, the model parameters and latent variables of the RDAM model can be estimated. After that, a monitoring strategy is designed based on the obtained results with both static and dynamic statistics. By this means, both the static deviation of the current sample and the temporal correlation within the process data can be effectively revealed. A simulated example and a real low-pressure heater process are adopted to illustrate the performance of the proposed RDAM method. Specifically, the proposed RDAM method is robust to the ambient noise and missing values, and it has better detection sensitivity for the process anomalies than the selected comparison methods.

Data-driven fault diagnosis methods have been widely applied at present. In actual processes, there usually exist multiple failure modes; the data frequency spectrum varies in different failure modes, which would bring challenges for feature extraction and subsequent fault diagnosis. In this article, a decision fusion scheme based on the mode decomposition and evidence theory is proposed for fault diagnosis during drilling. The raw data are decomposed into multiple series with different center frequencies, the decomposed series are reconstructed to several groups. For each group, the local diagnosis model is established, thus, several local diagnostic results are obtained. Then, all local diagnostic results are fed into the evidence theory-based decision fusion model. Meanwhile, a confidence matrices-based weight adjustment method is designed to enhance the reliability of fused results. An industrial case study based on the actual drilling data verifies that the proposed method is beneficial to improve the diagnostic effect during drilling.

Complicated geological environments lead to a high risk of drilling incidents. Early warning of loss and kick for the drilling process is essential to ensure process safety. On account of the nonlinear and temporal correlation of drilling parameters, an early warning method for loss and kick based on sparse autoencoder with multivariate time series is proposed. The sparse autoencoder is utilized for multivariate time series abnormality detection of the drilling process. Abnormal drilling parameter isolation is performed through contribution analysis. Reconstruction analysis and time series segmentation approaches are integrated for abnormal time series trend evaluation. The characteristic of drilling parameters under normal operation learned by the sparse autoencoder and the property of the original time series are taken into account. The final early warning result can be obtained through expert rules based on the trend evaluation result. Case studies are presented based on the data from an actual drilling project. The experiment result shows the effectiveness of the proposed method.

Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.

In real industrial applications, process data may get corrupted due to failure of the measurement devices or errors in data management. Besides, incipient faults, which may evolve into serious accidents, are generally more difficult to be detected because of its small magnitudes. In this article, a robust canonical variate dissimilarity analysis method is proposed to detect incipient faults for industrial processes with missing value. According to the low-rank characteristic, the low-rank matrix decomposition (LRMD) method is applied to recover the missing elements and reduce the ambient noise for process data. The output results of LRMD model consist of a low-rank component and a sparse component, which indicate main variance and residual information of the inputted data, respectively. For each component, a canonical variate analysis) model is developed to extract the temporal correlation in the process data. Based on the obtained features, a total of three monitoring statistics are established to reflect the operation status of the online sample. Among them, a statistic is used to measure the static deviation of this sample, and other two indices are applied to evaluate the dissimilarity between the past and future canonical variates. A simulated process and a real industrial process are adopted to illustrate the performance of the proposed method. Experimental results show that the proposed model can be well developed with incomplete training data and robustly detects the incipient faults for industrial applications.

The efficient mitigation of the detrimental effects of a fault in complex systems requires online fault diagnosis techniques that are able to identify the cause of an observable anomaly. However, an individual diagnosis model can only acquire a limited diagnostic effect and may be insufficient for a particular application. In this paper, a Bayesian network-based probabilistic ensemble learning (PEL-BN) strategy is proposed to address the aforementioned issue. First, an ensemble index is proposed to evaluate the candidate diagnosis models in a probabilistic manner so that the diagnosis models with better diagnosis performance can be selected. Then, based on the selected classifiers, the architecture of the Bayesian network can be constructed using the proposed three types of basic topologies. Finally, the advantages of different diagnosis models are integrated using the developed Bayesian network, and thus, the fault causes of the observable anomaly can be accurately inferred. In addition, the proposed method can effectively capture the mixed fault characteristics of multifaults (MFs) by integrating decisions derived from different diagnosis models. Hence, this method can also provide a feasible solution for diagnosing MFs in real industrial processes. A simulation process and a real industrial process are adopted to verify the performance of the proposed method, and the experimental results illustrate that the proposed PEL-BN strategy improves the diagnosis performance of single faults and is a feasible solution for MF diagnosis. Note to Practitioners-The focus of this paper is to develop a probabilistic ensemble learning strategy based on the Bayesian network (PEL-BN) to diagnose different kinds of faults in industrial processes. The PEL-BN strategy can automatically select the base classifiers to establish the architecture of the Bayesian network. In this way, the conclusions of these base classifiers can be effectively integrated to provide better diagnosis performance. In addition, the proposed method is also a feasible technique for diagnosing MFs resulted from the joint effects of multiple faults.

Due to the compensation of the control loops, industrial processes under feedback control generally reveal typical dynamic behaviors for different operation statuses. Conventional adaptive methods may update model falsely and thus result in invalid monitoring results, since they cannot effectively extract the feedback dynamic information and fail to accurately differentiate real anomalies from normal process changes. In this study, a recursive exponential slow feature analysis (ESFA) algorithm is developed for fine-scale adaptive monitoring to solve the problem of false model updating. First, an ESFA method is proposed to nonlinearly extract slow features, so that the general trend of the process variations can be better captured. On the basis of the ESFA model, a fine-scale adaptive monitoring scheme is developed to accurately capture the normal changes of industrial processes, including normal slow varying and normal shift of operation conditions. In this way, the normal slow varying can be effectively distinguished from incipient faults with unusual dynamic behaviors to avoid falsely adapting for the fault case, and the monitoring model can be correctly updated for new operation status after distinguishing real process anomalies from normal shifts of operation conditions. A simulation process and two real industrial processes are adopted to validate the performance of the proposed adaptive monitoring method. Experimental results show that the proposed method can effectively identify different operation statuses to decide whether to update the monitoring model or to raise an alarm.

In this paper, we proposed a new hyperspectral image target detector based on time series analysis, named as Exponential Smoothing Target Detector (ES-TD). As a classical method of time series analysis, the exponential smoothing method can choose the motional weight in the different part of data, which accelerates the reconstruction and forecast of the unknown data. The proposed method has a three-step process. Firstly, we select the applicable smoothing parameter according to the shape of the data curve. Then, given the reference and test spectral curves, we use the exponential smoothing method to obtain two new smoothing curves. Finally, we calculate the similarity between the two smoothing curves using SAM to determine whether the test spectral curve is the target or not. The proposed method has the feature of high computational efficiency and robustness. Experimental results on two real hyperspectral data sets demonstrate the advantages of the new method.

As the abundant spectral information of hyperspectral image, traditional pixel-wise classification methods is time-consuming in hyperspectral images. And purely pixel-wise classification methods often ignore lots of space information. In this paper, we investigate the usage of Kendall Concordant Coefficient (KCC) for region-dependent segmentation of the original hyperspectral data cube. The KCC-based method could combine spectral and spatial information effectively, and it has strong robustness with low complexity because it is a nonparametric method. We conduct a series of experiments, and draw conclusions that KCC-based method could obtain better segmentation and classification results than purely pixel-wise methods.