Fault detection in wind turbines is essential for ensuring their safety, reliability, and optimal performance. However, some of the existing approaches for sensor fault detection in wind turbines face several challenges that hinder their effectiveness. These challenges include handling multivariate and non-Gaussian data, low sensitivity to small changes, and setting an appropriate detection threshold to avoid false alarms. Additionally, constructing an analytical model for monitoring wind turbines becomes particularly challenging and time-consuming, especially for large-scale wind turbines. This paper proposes a semi-supervised data-driven approach for sensor fault detection in wind turbines using supervisory control and data acquisition (SCADA) data. The proposed approach combines the advantages of independent component analysis (ICA) and the Kantorovich Distance (KD)-based fault detection scheme. ICA enables efficient handling of multivariate non-Gaussian data, while the KD scheme provides a sensitive indicator for assessing the residuals obtained from ICA. The ICA-based KD scheme needs only fault-free data in training, making it more attractive for fault detection in practice. Kernel density estimation is employed to compute the detection threshold of the KD scheme, making it more flexible. Experimental evaluations using simulated sensor faults based on real wind turbine data demonstrate the superior detection performance of the proposed approach, achieving an average F1-score of approximately 0.96 and outperforming conventional approaches. 

Uncovering sensor faults in wind turbines: An improved multivariate statistical approach for condition monitoring using SCADA data

Real-time anomaly detection on time series of industrial furnaces: A comparison of autoencoder architectures

Principal component analysis (PCA) is a classic fault diagnosis method widely used in chemical process data modeling. However, the limitation of PCA to handle dynamic and time-variant data has been progressively exposed with the ever-increasing development of artificial intelligence technologies and applications of automation systems in the chemical industry. In this paper, an enhanced temporal algorithm-coupled optimized adaptive sparse PCA (ETA-OASPCA) methodology is proposed with the aim to improve the conventional PCA method by introducing temporal state computation and dynamic adaptive sparsity. The ETA is constructed to extract the state transitions of the time-variant process data by introducing the “Transition” and “Hold”, acquire the region of interest by employing a Maximum Fuzzy c-means (MFCM) method, and then use the region time prediction automaton to label the possible abnormal states. The introduction of dynamic adaptive sparsity in PCA, achieved by presenting an iterative interior point optimization method to update the dynamic sparse penalty term, can impose sparsity constraints on dynamic data calculations, thus enhancing the interpretability of the PCA model. The effectiveness of the proposed method was validated through its application in the Eastman Tennessee process, showing that an average fault detection rate, false alarm rate, and latency of 99.4%, 0.37%, and 4.11 mins can be achieved, respectively. 

An Enhanced Temporal Algorithm- Coupled Optimized Adaptive Sparse Principal Component Analysis Methodology for Fault Diagnosis of Chemical Processes

Deep learning-based fault detection and diagnosis (FDD) methods have received considerable attention, and many methods based on convolutional neural network (CNN) have been applied to fault diagnosis for chemical processes. However, current fault diagnosis methods train and detect all faults using a single model and the same feature inputs, resulting in the neglect of correlations and difference between faults and inferior fault diagnosis performance. In this study, a novel fault diagnosis method named triage-based convolutional neural network (TrCNN) for fault diagnosis is proposed. Initially, the fault set is partitioned into distinct triage types. Subsequently, distinct models are formulated and applied to their respective triage types in the sub networks layer, while a triage network is developed in the triage layer. Ultimately, the models from the triage layer and sub networks layer come together to constitute the triage fault diagnosis system. The proposed method can adaptively select suitable models and features for different triage types, leading to improved diagnostic accuracy, especially for similar faults. When applied to the Tennessee Eastman (TE) chemical process the TrCNN demonstrates impressive performance, validating its effectiveness in fault diagnosis. 

A novel triage-based fault diagnosis method for chemical process

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations.

/pdf/causal-plot-causal-based-fault-diagnosis-method-based-on-2creudzr.pdf

Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis

Anomaly detection and early warning of the blast furnace ironmaking process is an important research direction of blast furnace production. In this paper, through the analysis and optimization of the multivariate statistical process control (MSPC) model, MSPC based on TOPSIS and the grey (GT-MSPC) model is established based on the problems of missing alarms, false alarms and untimely prediction in the abnormal detection process of the traditional blast furnace ironmaking production process. First, a composite volatility (CV) that consists of the squared prediction error and T2 is computed to form the MSPC model. The parameters of different training set sizes and different alarm conditions are optimized by using the technique for order preference by similarity to an ideal solution model to optimize the effect of anomaly detection and fault diagnosis. Second, according to the results of the contribution plot, the fault diagnosis of abnormal occurrence is carried out, the main influencing factors of abnormal occurrence are analysed, and the monitoring is strengthened. Finally, the grey model is used to predict CV to realize anomaly early warning and form the GT-MSPC model. By collecting the field data of blast furnace production and performing empirical analysis, the results show that the GT-MSPC model has higher anomaly detectability. The detection rate is 50 % higher than that of the manual observation method, and the GT-MSPC model alarms 16.4861 min earlier than manual detection method. Fault diagnosis can accurately locate the main influencing factors of blast furnace anomalies, such as the furnace body static pressure and differential pressure. In summary, the GT-MSPC model realizes the accurate identification and prediction of anomalies and greatly reduces the occurrence of missing and false alarms and untimely prediction in the blast furnace system, thus reducing the risk of blast furnaces. It can be used to assist field engineers in dealing with abnormal faults and improve the product quality and production safety of blast furnace ironmaking. 

Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

� Abstract—The measurement errors in Virtual Reference Station (VRS) technology are related to the natural circumstances and geographical conditions of different areas. The chief aim of this paper is to study influence factors of VRS errors. Contingency tables were applied to analyze the correlation of those error factors. The example presented was used for describing how chi-square test and likelihood ratio test were used to isolate problems in errors correlation analysis. This investigation preliminarily proves that the contingency table may be utilized in VRS error research.

Application of Statistical Methods: To Analyze Correlation of VRS Errors

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Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

Application of Statistical Methods: To Analyze Correlation of VRS Errors