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Practical industrial processes usually have nonstationary properties, which make the monitoring more challenging because the fault information may be buried by nonstationary trends. For nonstationary processes, many methods have been proposed for fault detection based on continuous variables. However, binary variables may appear together with continuous variables in modern industrial processes. To address the issue of process monitoring with hybrid variables and nonstationarity, a model named recursive hybrid variable monitoring (RHVM) is proposed in this paper. For RHVM, recursive strategy is utilized to suppress nonstationary trend and to reveal fault information. In addition, RHVM has the ability of model self-updating with arriving samples. The closed-form updates of required parameters are derived in detail and the improvement of performance is analyzed. At last, the superiority of the proposed model is demonstrated by a simulation example and a practical nonstationary process of a power plant. [ABSTRACT FROM AUTHOR]

In this article, the problem of intermittent fault (IF) detection is investigated for linear stochastic systems over sensor networks, where the appearing and disappearing times, and magnitude of IF are all nondeterministic. By utilizing the moving-horizon estimator, a novel residual generator is designed to realize the distributed detection of IFs in sensor networks. Different from the traditional moving horizon estimation algorithms, weight matrices of the quadratic cost function in this article are regulated by an unreliability index of the prior estimate to suppress the smearing effect of IFs. In virtue of the matrix transformation method and statistical theory, estimator parameters are obtained and the detectability of a single IF is analyzed by using the residual. In order to avoid the collisions of detection results from different residuals, the global detectability condition is given for all IFs. A cooperative decision-making strategy is proposed such that the only detection result can be guaranteed, which includes the appearing and disappearing times of IFs, and the nodes suffering from IFs. Finally, an illustrative example is provided to show the feasibility and effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) and its variants have been widely used for process monitoring and quality control. A key issue of PCA-related methods is to select an appropriate number of principal components (PCs). However, few approaches for component selection consider the monitoring performance, and they usually rely on prior fault information. This article develops an effective algorithm for dynamic component selection, which selects components for each sample based on a detection performance index, without need for prior fault information. Component selection is transformed into a stochastic optimization problem, whose optimal solution is derived analytically. Then, a subset of components that are sensitive to faults is obtained. The proposed method reduces the requirement for the detectable fault amplitude, which leads to better performance. Furthermore, the differences between PCA and the proposed method are discussed, and online computational complexity is analyzed. Case studies on a continuous stirred tank heater (CSTH), the Tennessee Eastman process (TEP), and a real ultra-supercritical power plant demonstrate that the proposed method has better monitoring performance than PCA and some of its variants. [ABSTRACT FROM AUTHOR]

The ideal air brake system is to provide accurate brake pressures, which ensures the normal braking performance of high-speed trains. For the high-speed trains in China, a univariate monitoring strategy has been adopted to monitor the state of each brake cylinder of the air brake system separately. In practice, this kind of strategy is effective for faults with large amplitude, but it is always ineffective for all kinds of incipient faults with small amplitude. Due to nonstationary and multimode properties inherent in air brake systems, conventional detection methods are difficult to be used. In this article, we develop novel detection strategies for three kinds of incipient faults in the air brake systems, including sensor deviation fault, leakage fault, and brake cylinder component fault. Starting from the characteristics of actual data from air brake systems, some variables with high level of consistency are selected. Then, a linear data transformation is conducted to obtain approximate stationary properties, and all the data in multiple modes are mapped into a compact domain. In addition, a fault-to-signal ratio (FSR) is proposed to reflect the sensitivity of the proposed detection statistic to the fault. In particular, the multistage control limits and sliding window strategies are integrated into the detection statistic, which effectively lowers the control limits and enhances robustness to noise, respectively. Finally, experiments on the braking test platform are provided to show the effectiveness of our proposed strategies. [ABSTRACT FROM AUTHOR]

Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method. [ABSTRACT FROM AUTHOR]

In this paper, the detection problem of intermittent multiplicative sensor fault is investigated for stochastic uncertain systems. A robust optimal filter is designed according to the criterion of minimum estimation error covariance. Then, based on this, a residual generator is constructed, and the quantitative effect of the fault on it is discussed in detail. After that we design the evaluation function and detection threshold to achieve intermittent fault detection. Our proposed strategy has a recursive form and only includes simple arithmetic operations, thus it is suitable for real‐time online applications. Finally, a simulation example is given to demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper studies the fault detection problem for a class of discrete-time switched linear systems under dwell-time (DT) constraints, using the parity space-based approach. The DT-dependent Lyapunov function is employed to investigate the asymptotic stability with less conservatism and to solve the constant ${l}_{2}$ -gain performance analysis problem, and its advantage is verified compared to the time-independent Lyapunov function approach. The corresponding switching residual generation is made to carry out the desired fault detection in the framework of parity space-based model. Then, by means of solving a generalized eigenvalue–eigenvector problem, the parity space matrices design is implemented. A quantitative relationship is established between the optimization performance and the choice of the parity space order. Two numerical examples are utilized to demonstrate effectiveness of the developed fault detection approach, including an application to switched RLC circuits. [ABSTRACT FROM AUTHOR]

In this paper, we focus our study on analysis of fault diagnosis performance for satellite attitude control systems subject to $l_{2}$ -norm-bounded process disturbances and measurement noises, which concerns with fault detectability and fault isolability. For an observer-based fault detection (FD), a major concern is to answer if the choice of a threshold satisfies an acceptable trade-off between fault detection rate (FDR) and false alarm rate (FAR). The smaller a threshold is, the better is the FDR, but the poorer is the FAR. In addition to this, knowledge of fault isolability is useful for answering how difficult it is to isolate a fault from another one. The main contributions of this paper are the probabilistic performance evaluation of the FD system in the context of FAR and a contribution analysis-based method of fault isolation. First, an extended $H_{i}/H_{\infty }$ optimization-based FD scheme is applied to the satellite attitude control systems and a recursive algorithm is presented to the implementation of online FD. Second, regarding the uncertain statistical characteristics of the unknown inputs, randomized algorithms are developed to verify the achievable FAR for a prescribed given threshold. Especially, without knowing the $l_{2}$ -norm boundedness of the unknown inputs, a probabilistic estimation of worst case threshold is also obtained to guarantee an acceptable level of FAR. Third, a contribution analysis-based method of fault isolation is proposed for satellite attitude control systems. Finally, the effectiveness of the proposed algorithms is verified through a satellite attitude control system. [ABSTRACT FROM AUTHOR]

• A new multimode process monitoring method CIBL-RTCSA is proposed. • CIBL is proposed for mode identification, with higher accuracy than traditional classification methods. • A multiple RTCSA model with a window-switching scheme is developed for diagnosing the incipient sensor fault in multimode processes. • A numerical example and simulation on a CSTH both demonstrate the effectiveness of the proposed method. This paper considers the problem of detecting and isolating incipient sensor fault in multimode processes. A data-driven multimode process monitoring method called conditionally independent Bayesian learning based recursive transformed component statistical analysis (CIBL-RTCSA) is presented. Considering the strong assumption of conditional independence in naïve Bayes, orthogonal transformation is applied to measured variables to improve the extent of conditional independence in different operating modes. The Bayes-based mode identification is adopted for transformed data, and a multiple RTCSA model with a window-switching scheme is developed for monitoring multimode processes. With the orthogonal transformation, the accuracy of mode identification can be effectively improved compared with naïve Bayes. In addition, the fault detection and isolation performance of the proposed method outperforms traditional monitoring methods. The effectiveness of the proposed method is demonstrated by a numerical example and the simulation on a continuous stirred tank heater process. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method. Highlights • New time-constrained sparse representation method is put forward. • Novel robust characteristic dimensionality reduction method is proposed. • Online fault detection algorithm is developed and fault detectability is analyzed. • Experimental studies on TEP and EMU braking system validate the proposed methods. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the event‐triggered filtering and intermittent fault detection problems are investigated for a class of time‐varying systems with stochastic parameter uncertainty and sensor saturation. Due to the existence of event‐triggered mechanism, the measured signal could be transmitted only when it satisfies the triggering condition. An event‐triggered filter is developed, which takes the event‐triggered mechanism, parameter uncertainty, and sensor saturation into full consideration but does not depend on any specific uncertainty structure. By utilizing the inductive and stochastic analysis technique, the filter gain is designed to ensure that the upper bound of the estimation error covariance is minimized at each time step. Based on the proposed filter, a residual is generated and the corresponding evaluation function and detection threshold are given to achieve fault detection. At last, two simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Air brake systems are crucial systems for safe and stable operation of electric multiple units (EMUs). The brake cylinder system, which includes brake cylinders, corresponding pressure sensors, and connection pipes, plays a vital role in the EMU air brake system. This is because brake cylinder pressures directly affect the brake operation. Currently, brake cylinder pressures are monitored by univariate control charts, i.e., the brake cylinder system will be considered as faulty if a certain pressure goes beyond its allowed range. Besides, serious sensor hardware faults such as open circuit or short circuit can also be detected by system self-inspection circuits. However, three kinds of faults, including brake cylinder component fault, soft sensor fault, as well as gas leakage fault, cannot be well handled by current monitoring methods if these faults are not very serious. In this paper, a fault detection index called intervariable variance (IVV) is first presented to perform fault detection for these faults. Fault detectability analysis is provided, and the IVV statistic is also compared with the univariate control chart approach. Then, a fault isolation strategy is proposed to distinguish different kinds of faults and determine the location of the occurred fault. Finally, the effectiveness of the proposed fault detection and isolation method is demonstrated via extensive experimental studies that are carried out on the EMU brake test bench of Qingdao Sifang Rolling Stock Research Institute Co., Ltd., China. [ABSTRACT FROM AUTHOR]

This paper presents a new data-driven fault detection method called covariance eigenpairs neighbour distance (CEND) for monitoring chemical processes. It processes measured data in one-step sliding windows to estimate covariance, and the eigenpairs of each sample covariance matrix are recursively calculated using rank-one modification. The eigenvalues and selected elements of eigenvectors are stacked into reference vectors. For each window data containing the latest measurement vector, the neighbour distance of eigenpairs from the reference matrix can be effectively calculated by virtue of k-d tree. The neighbour distance serves as the detection index, whose control limit can be determined by validation dataset with assigning a significance level. A high neighbour distance exceeding the control limit implies the occurrence of fault. Simulations on the continuous stirred tank reactor (CSTR) and the Tennessee Eastman process (TEP) both indicate the superior fault detectability of the proposed method, compared with conventional multivariate statistical process monitoring (MSPM) methods such as principal component analysis (PCA) and independent component analysis (ICA). [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic approach to fault detection (FD) for nonlinear systems subject to l2[0,N]-norm bounded unknown input. The major contribution is to design an evaluation function for robust FD in a unified framework of l2-norm estimation of unknown input and determine a threshold based on probabilistic analysis of FD performance. The problem of robust FD is first formulated as to find a minimal estimation of the l2[0,N]-norm of unknown input including unknown initial state. It is shown that such an estimation leads to a unified design of evaluation function for FD using extended Kalman filter or Hi/H\infty optimization-based FD filter. Based on this, a probabilistic approach to threshold determination and FD performance verification is proposed. In particular, if the l2[0,N]-norm boundedness of unknown input is not available, a choice of threshold can be made in the framework of probabilistic analysis for achieving a tradeoff between false alarm rate and FD rate. Finally, a nonlinear UAV control system model is given to demonstrate the effectiveness of the proposed method and show the feasibility of practical application. [ABSTRACT FROM AUTHOR]

This paper focuses on the fault-tolerant control problem for an Internet-based three-tank system in the presence of possible sensor bias faults. The Internet-based three-tank system is an experimental setup that can be regarded as a typical networked system for evaluating networked fault-diagnosis and fault-tolerant control methods. Packet dropout phenomenon in the sensor-to-controller link is considered in this paper, and the fault type we deal with is chosen as the sensor bias fault. Fault-diagnosis unit is designed toward an auxiliary system. Sensor bias faults can be detected by comparing the residual signal generated by the fault detection filter and a prescribed threshold. After that, the fault can be isolated by using the residual analysis approach. Once the fault is isolated, it can be estimated iteratively in the least-squares sense. A fault accommodation method is proposed, and a fault-tolerant control strategy is achieved based on the fault information provided by the fault-diagnosis unit. The approach brought forward in this paper is demonstrated via an experimental study on the practical Internet-based three-tank system. Results show the effectiveness and the applicability of the proposed techniques. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

This special section on "Diagnosis and Prognosis for Complicated Industrial Systems?Part II? is the continuation of its previous part I (IEEE Transactions on Industrial Electronics, Vol. 63, Issue 4, 2016). The diagnosis and prognosis approaches can be roughly classified into two categories: 1) model-based and 2) data-driven approaches. The model-based approaches perform the diagnosis and prognosis relying on the physical models from first principles, whereas the data-driven approaches make full use of the process data to monitor the systems under consideration. Due to the increasing complexity of the industrial system, new challenges are encountered in diagnosis and prognosis of such complicated systems, and more efficient process monitoring approaches are needed. From this point of view, the papers included in this section are focused on the recent developments of diagnosis and prognosis for complicated industrial systems, providing novel ideas and referential directions for both the academic and industrial communities. [ABSTRACT FROM PUBLISHER]

Intermittent faults (IFs) have properties such as intermittency, random magnitude and random duration time. Hence the detection of IFs means: (i) to detect not only all the appearing time but also all the disappearing time of IFs and (ii) to detect the appearing time of an IF before this IF disappears, and the disappearing time of an IF before the subsequent IF appears. Within a statistical framework, the detection of scalar IFs in continuous linear stochastic dynamic systems has been mainly studied. Based on the sliding window, an analytical residual is generated, and two hypothesis tests are implemented to detect the appearing and disappearing times of IFs. In addition, a necessary and sufficient condition for the detectability of IFs is obtained, and the detection speed can be fast enough. Theoretical analysis and numerical simulations fully verify that IFs can be successfully detected. [ABSTRACT FROM AUTHOR]

Dynamic principal component analysis (DPCA) is widely used in the monitoring of dynamic multivariate processes. In traditional DPCA where a time window is used, the dynamic relations among process variables are implicit and difficult to interpret in terms of variables. To extract explicit latent variables that are dynamically correlated, a dynamic latent-variable model is proposed in this paper. The new structure can improve the modeling and the interpretation of dynamic processes and enhance the performance of monitoring. Fault detection strategies are developed, and contribution analysis is available for the proposed model. The case study on the Tennessee Eastman Process is used to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

Multivariate statistical process monitoring technologies, including principal component analysis (PCA) and partial least squares (PLS), have been successfully applied in many industrial processes. However, in practice, many PCA alarms do not lead to quality deterioration due to process control and recycle loops in process flowsheets, which hinders the reliability of PCA-based monitoring methods. Therefore, one is more interested to monitor the variations related to quality data, and detect the faults which affect quality data. Recently, a total projection to latent structures (T-PLS) model has been reported to detect output-relevant faults. In this paper, a generalized reconstruction based contribution (RBC) method with T-PLS model is proposed to diagnose the fault type for output-relevant faults. Furthermore, the geometrical property of generalized RBC is analyzed. A detailed case study on the Tennessee Eastman process is presented to demonstrate the use of the proposed method without or with prior knowledge. [ABSTRACT FROM PUBLISHER]

The article presents a study which proposed a total projection to latent structures (T-PLS) algorithm. The properties of T-PLS are studied, including its relationship to the orthogonal partial least squares or projection to latent structures (PLS). The purpose of T-PLS is to acquire a further decomposition on X-space which is more suitable for process monitoring. The T-PLS is proven to be an oblique space decomposition of X- Space. Simulation examples show the effectiveness of T-PLS based fault detection methods.

This article deals with the fault detection problem for a class of discrete-time networked systems with multiple state delays and unknown input. Two kinds of incomplete measurements, namely measurements with random communication delays and measurements with stochastic packet losses, are simultaneously considered. By properly augmenting the states of the original system and the fault detection filter, the fault detection filter design problem can be formulated as an H∞ filtering problem. Attention is focused on the design of a fault detection filter such that, for all unknown input and incomplete measurements, the error between residual and weighted fault is made as small as possible. A sufficient condition for the existence of the desired fault detection filter is established in terms of a linear matrix inequality. A numerical example is provided to illustrate the effectiveness and applicability of the proposed techniques. [ABSTRACT FROM AUTHOR]

This paper deals with the event-based fault detection problem for T–S fuzzy systems with packet dropouts as well as state- and disturbance-dependent noises (( x, v )-dependent noises for short). To utilize limited network resources efficiently, the event-triggered mechanism is employed such that only selected valuable data is transmitted to the filter according to whether specific events happen or not. The aim of the addressed problem is to design a fuzzy fault detection filter which guarantees the stochastic stability of the overall fault detection dynamics as well as the robustness against disturbances and sensitivity to faults of the residual signal. Some sufficient criteria are obtained for the existence of the desired fault detection filter which can be designed by solving a set of linear matrix inequalities. Finally, a numerical example is presented to illustrate the validity of the derived results. [ABSTRACT FROM AUTHOR]

This paper addresses the detection problem of intermittent sensor faults for linear time-varying (LTV) systems with stochastic uncertainties. A robust filter is proposed which has advantages of zero mean and minimum state estimation error covariance. Then a corresponding residual generator is constructed and the quantitative influence of sensor faults on it is analyzed. Next, we design the evaluation function and detection threshold to achieve intermittent fault detection (IFD). Besides, the detectability of sensor faults is also provided. Finally, a simulation study is carried out to illustrate the effectiveness and applicability of our proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address fault detection for networked control systems subject to random packet dropout. The packet dropout is assumed to be existing in the sensor-to-controller link and the controller-to-actuator link. Both parity space and observer based residual generation and evaluation approaches are proposed. In parity space based fault detection scheme, a new optimization index is proposed to deal with stochastic system parameters caused by random packet dropout, while in observer based scheme, this is accomplished by introducing a reference model. In order to evaluate performance of the designated threshold, the corresponding false alarm rate is given. The two fault detection schemes can ensure both robustness to packet dropout as well as disturbance and sensitivity to fault. An experimental study is employed to verify that the proposed method performs better than the existing approaches. [Copyright &y& Elsevier]