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Deep learning (DL), regarded as a breakthrough machine learning technique, has proven to be effective for a variety of real-world applications. However, DL has not been actively applied to condition monitoring of industrial assets, such as gas turbine combustors. We propose a deep semi-supervised anomaly detection (deepSSAD) that has two key components: (1) using DL to learn representations or features from multivariate, time-series sensor measurements; and (2) using one-class classification to model normality in the learned feature space, thus performing anomaly detection. Both steps use normal data only; thus our anomaly detection falls into the semi-supervised anomaly detection category, which is advantageous for industrial asset condition monitoring where abnormal or faulty data is rare. Using the data collected from a real-world gas turbine combustion system, we demonstrate that our proposed approach achieved a good detection performance (AUC) of 0.9706 ± 0.0029. Furthermore, we compare the detection performance of the proposed approach against that of other different designs, including different features (i.e., the deep learned, handcrafted and PCA features) and different detection models (i.e., one-class ELM, one-class SVM, isolation forest, and Gaussian mixture model). The proposed approach significantly outperforms others. The proposed combustor anomaly detection approach is effective in detecting combustor anomalies or faults.
Weizhong Yan. Detecting Gas Turbine Combustor Anomalies Using Semi-supervised Anomaly Detection with Deep Representation Learning. Cognitive Computation 2019, 12, 398 -411.
AMA StyleWeizhong Yan. Detecting Gas Turbine Combustor Anomalies Using Semi-supervised Anomaly Detection with Deep Representation Learning. Cognitive Computation. 2019; 12 (2):398-411.
Chicago/Turabian StyleWeizhong Yan. 2019. "Detecting Gas Turbine Combustor Anomalies Using Semi-supervised Anomaly Detection with Deep Representation Learning." Cognitive Computation 12, no. 2: 398-411.
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.
Weizhong Yan; Lijie Yu. On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach. 2019, 1 .
AMA StyleWeizhong Yan, Lijie Yu. On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach. . 2019; ():1.
Chicago/Turabian StyleWeizhong Yan; Lijie Yu. 2019. "On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach." , no. : 1.
Cyber-physical systems (CPS) security has become a critical research topic as more and more CPS applications are making increasing impacts in diverse industrial sectors. Due to the tight interaction between cyber and physical components, CPS security requires a different strategy from the traditional Information Technology (IT) security. In this paper, we propose a machine learning-based attack detection scheme, as part of our overall CPS security strategies. The proposed scheme performs attack detection at the physical layer by modeling and monitoring physics or physical behavior of the physical asset or process. In developing the proposed attack detection scheme, we devote our efforts on intelligently deriving salient signatures or features out of the large number of noisy physical measurements by leveraging physical knowledge and using advanced machine learning techniques. Such derived features not only capture the physical relationships among the measurements, but also have more discriminant power in distinguishing normal and attack activities. In our experimental study for demonstrating the effectiveness of the proposed attack detection scheme, we consider heavy-duty gas turbines of combined cycle power plants as the CPS application. Using the data from both the high-fidelity simulation and several real plants, we demonstrate that our proposed attack detection scheme is effective in early detection of attacks or malicious activities.
Weizhong Yan; Lalit K. Mestha; Masoud Abbaszadeh. Attack Detection for Securing Cyber Physical Systems. IEEE Internet of Things Journal 2019, 6, 8471 -8481.
AMA StyleWeizhong Yan, Lalit K. Mestha, Masoud Abbaszadeh. Attack Detection for Securing Cyber Physical Systems. IEEE Internet of Things Journal. 2019; 6 (5):8471-8481.
Chicago/Turabian StyleWeizhong Yan; Lalit K. Mestha; Masoud Abbaszadeh. 2019. "Attack Detection for Securing Cyber Physical Systems." IEEE Internet of Things Journal 6, no. 5: 8471-8481.
Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.
Jiao Liu; Jinfu Liu; Daren Yu; Myeongsu Kang; Weizhong Yan; Zhongqi Wang; Michael G. Pecht. Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network. Energies 2018, 11, 2149 .
AMA StyleJiao Liu, Jinfu Liu, Daren Yu, Myeongsu Kang, Weizhong Yan, Zhongqi Wang, Michael G. Pecht. Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network. Energies. 2018; 11 (8):2149.
Chicago/Turabian StyleJiao Liu; Jinfu Liu; Daren Yu; Myeongsu Kang; Weizhong Yan; Zhongqi Wang; Michael G. Pecht. 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network." Energies 11, no. 8: 2149.
Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.
De-Mi Cui; Weizhong Yan; Xiao-Quan Wang; Lie-Min Lu. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques. Sensors 2017, 17, 2443 .
AMA StyleDe-Mi Cui, Weizhong Yan, Xiao-Quan Wang, Lie-Min Lu. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques. Sensors. 2017; 17 (11):2443.
Chicago/Turabian StyleDe-Mi Cui; Weizhong Yan; Xiao-Quan Wang; Lie-Min Lu. 2017. "Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques." Sensors 17, no. 11: 2443.
The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.
Jing-Kui Zhang; Weizhong Yan; De-Mi Cui. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines. Sensors 2016, 16, 447 .
AMA StyleJing-Kui Zhang, Weizhong Yan, De-Mi Cui. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines. Sensors. 2016; 16 (4):447.
Chicago/Turabian StyleJing-Kui Zhang; Weizhong Yan; De-Mi Cui. 2016. "Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines." Sensors 16, no. 4: 447.