arXiv subjects

Donghua Zhou

Publications and source records attributed to Donghua Zhou.

14 recordsLinked to original sources

arXiv2026

Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios

With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.

cs.RO↗

arXiv2026

Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions

Three-phase asynchronous motor are fundamental components in industrial systems, and their failure can lead to significant operational downtime and economic losses. Vibration and current signals are effective indicators for monitoring motor health and diagnosing faults. However, motors in real applications often operate under variable conditions such as fluctuating speeds and loads, which complicate the fault diagnosis process. This paper presents a comprehensive dataset collected from a three-phase asynchronous motor under various fault types and severities, operating under diverse speed and load conditions. The dataset includes both single faults and mechanical-electrical compound faults, such as rotor unbalance, stator winding short circuits, bearing faults, and their combinations. Data were acquired under both steady and transitional conditions, with signals including triaxial vibration, three-phase currents, torque, and key-phase signals. This dataset supports the development and validation of robust fault diagnosis methods for electric motors under realistic operating conditions.

eess.SY↗

arXiv2024

Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, standard evaluation metrics and typical real-world application scenarios are summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed.

eess.SY↗

arXiv2024

Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions

The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods.

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arXiv2022

Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring

In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.

cs.LG↗

arXiv2022

Hybrid variable monitoring: An unsupervised process monitoring framework with binary and continuous variables

Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al., are strongly dependent on continuous variables because most of them inevitably involve Euclidean or Mahalanobis distance. With industrial processes becoming more and more complex and integrated, binary variables also appear in monitoring variables besides continuous variables, which makes process monitoring more challenging. The aforementioned traditional approaches are incompetent to mine the information of binary variables, so that the useful information contained in them is usually discarded during the data preprocessing. To solve the problem, this paper focuses on the issue of hybrid variable monitoring (HVM) and proposes a novel unsupervised framework of process monitoring with hybrid variables including continuous and binary variables. HVM is addressed in the probabilistic framework, which can effectively exploit the process information implicit in both continuous and binary variables at the same time. In HVM, the statistics and the monitoring strategy suitable for hybrid variables with only healthy state data are defined and the physical explanation behind the framework is elaborated. In addition, the estimation of parameters required in HVM is derived in detail and the detectable condition of the proposed method is analyzed. Finally, the superiority of HVM is fully demonstrated first on a numerical simulation and then on an actual case of a thermal power plant.

stat.ME↗

arXiv2022

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration, a great number of two-valued variables (generally stored in the form of 0 or 1 value) often exist in large-scale industrial processes. However, these variables cannot be effectively handled by traditional monitoring methods such as LDA, PCA and PLS. Recently, a mixed hidden naive Bayesian model (MHNBM) is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring. Although MHNBM is effective, it still has some shortcomings that need to be improved. For MHNBM, the variables with greater correlation to other variables have greater weights, which cannot guarantee greater weights are assigned to the more discriminating variables. In addition, the conditional probability must be computed based on the historical data. When the training data is scarce, the conditional probability between continuous variables tends to be uniformly distributed, which affects the performance of MHNBM. Here a novel feature weighted mixed naive Bayes model (FWMNBM) is developed to overcome the above shortcomings. For FWMNBM, the variables that are more correlated to the class have greater weights, which makes the more discriminating variables contribute more to the model. At the same time, FWMNBM does not have to calculate the conditional probability between variables, thus it is less restricted by the number of training data samples. Compared with MHNBM, FWMNBM has better performance, and its effectiveness is validated through the numerical cases of a simulation example and a practical case of Zhoushan thermal power plant (ZTPP), China.

stat.ME↗

arXiv2021

Self-learning sparse PCA for multimode process monitoring

This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes, where synaptic intelligence is employed to measure the importance of variables and a regularization term is added to preserve the learned knowledge of previous modes. Different from traditional multimode monitoring methods, the monitoring model is updated based on the current model and new data when a new mode arrives, thus delivering prominent performance for sequential modes. Besides, the computation and storage resources are saved in the long run, because it is not necessary to retrain the model from scratch frequently and store data from previous modes. More importantly, the model furnishes excellent interpretability owing to the sparsity of parameters. Finally, a numerical case and a practical pulverizing system are adopted to illustrate the effectiveness of the proposed algorithm.

cs.LG↗

arXiv2021

Monitoring multimode processes: a modified PCA algorithm with continual learning ability

For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effective manner to make local monitoring model remember the features of previous modes. Choosing the principal component analysis (PCA) as a basic monitoring model, we try to resolve this problem. A modified PCA algorithm is built with continual learning ability for monitoring multimode processes, which adopts elastic weight consolidation (EWC) to overcome catastrophic forgetting of PCA for successive modes. It is called PCA-EWC, where the significant features of previous modes are preserved when a PCA model is established for the current mode. The optimal parameters are acquired by differences of convex functions. Moreover, the proposed PCA-EWC is extended to general multimode processes and the procedure is presented. The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm. Potential limitations and relevant solutions are pointed to understand the algorithm further. Numerical case study and a practical industrial system in China are employed to illustrate the effectiveness of the proposed algorithm.

stat.ML↗

arXiv2021

Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation

This paper considers the problem of nonstationary process monitoring under frequently varying operating conditions. Traditional approaches generally misidentify the normal dynamic deviations as faults and thus lead to high false alarms. Besides, they generally consider single relatively steady operating condition and suffer from the catastrophic forgetting issue when learning successive operating conditions. In this paper, recursive cointegration analysis (RCA) is first proposed to distinguish the real faults from normal systems changes, where the model is updated once a new normal sample arrives and can adapt to slow change of cointegration relationship. Based on the long-term equilibrium information extracted by RCA, the remaining short-term dynamic information is monitored by recursive principal component analysis (RPCA). Thus a comprehensive monitoring framework is built. When the system enters a new operating condition, the RCA-RPCA model is rebuilt to deal with the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to settle the `catastrophic forgetting' issue inherent in RPCA, where significant information of influential parameters is enhanced to avoid the abrupt performance degradation for similar modes. The effectiveness of the proposed method is illustrated by a practical industrial system.

eess.SY↗

arXiv2020

Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rm SPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.

stat.ME↗

arXiv2020

Detection of Intermittent Faults Based on an Optimally Weighted Moving Average T^2 Control Chart with Stationary Observations

The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling's $T^2$ statistic to form an OWMA $T^2$ control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven.

cs.IT↗

arXiv2020

Detection and Detectability of Intermittent Faults Based on Moving Average T2 Control Charts with Multiple Window Lengths

So far, problems of intermittent fault (IF) detection and detectability have not been fully investigated in the multivariate statistics framework. The characteristics of IFs are small magnitudes and short durations, and consequently traditional multivariate statistical methods using only a single observation are no longer effective. Thus in this paper, moving average T^2 control charts (MA-TCCs) with multiple window lengths, which simultaneously employ a bank of MA-TCCs with different window lengths, are proposed to address the IF detection problem. Methods to reduce false/missing alarms and infer the IFs' appearing and disappearing time instances are presented. In order to analyze the detection capability for IFs, definitions of guaranteed detectability are introduced, which is an extension and generalization of the original fault detectability concept focused on permanent faults (PFs). Then, necessary and sufficient conditions are derived for the detectability of IFs, which may appear and disappear several times with different magnitudes and durations. Based on these conditions, some optimal properties of two important window lengths are further discussed. In this way, a theoretical framework for the analysis of IFs' detectability is established as well as extended discussions on how the theoretical results can be adapted to real-world applications. Finally, simulation studies on a numerical example and the continuous stirred tank reactor (CSTR) process are carried out to show the effectiveness of the developed methods.

eess.SY↗

arXiv2020

Detection and Isolation of Wheelset Intermittent Over-creeps for Electric Multiple Units Based on a Weighted Moving Average Technique

Wheelset intermittent over-creeps (WIOs), i.e., slips or slides, can decrease the overall traction and braking performance of Electric Multiple Units (EMUs). However, they are difficult to detect and isolate due to their small magnitude and short duration. This paper presents a new index called variable-to-minimum difference (VMD) and a new technique called weighted moving average (WMA). Their combination, i.e., the WMA-VMD index, is used to detect and isolate WIOs in real time. Different from the existing moving average (MA) technique that puts an equal weight on samples within a time window, WMA uses correlation information to find an optimal weight vector (OWV), so as to better improve the index's robustness and sensitivity. The uniqueness of the OWV for the WMA-VMD index is proven, and the properties of the OWV are revealed. The OWV possesses a symmetrical structure, and the equally weighted scheme is optimal when data are independent. This explains the rationale of existing MA-based methods. WIO detectability and isolability conditions of the WMA-VMD index are provided, leading to an analysis of the properties of two nonlinear, discontinuous operators, $\min$ and $\textrm{VMD}_i$. Experimental studies are conducted based on practical running data and a hardware-in-the-loop platform of an EMU to show that the developed methods are effective.

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