Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

Engineered Conditioning (EC) is a Genetic Algorithm operator that works together with the typical genetic algorithm operators: mate selection, crossover, and mutation, in order to improve convergence toward an optimal multiple fault diagnosis. When incorporated within a typical genetic algorithm, the resulting hybrid scheme produces improved reliability by exploiting the global nature of the genetic algorithm as well as “local” improvement capabilities of the Engineered Conditioning operator.

In this paper, a new approach to alarm processing in power systems is proposed based on the set covering theory and a refined genetic algorithm (RGA). Although alarm processing and fault diagnosis have different explanations in power system community, from the viewpoint of artificial intelligence community, the alarm processing problem is a typical Multiple Fault Diagnosis (MFD) problem. Thus, we first attribute the alarm processing problem to a MFD problem, and discuss the MFD problem in general with special emphasis on the evaluation criteria. And then, a probabilistic criterion is introduced to describe the alarm processing problem, and this criterion is more reasonable man the currently used criteria. Thirdly, a new method is presented to solve the alarm processing problem using a RAG. Finally, two sample examples are used to demonstrate the feasibility and efficiency of the developed method. Test results suggest that the developed RAG-based probabilistic method to the alarm processing problem is promising.

Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

The problem of fault detection and diagnosis (FDD) in dynamic systems has received considerable attention in last decades due to the growing complexity of modern engineering systems and ever increasing demand for fault tolerance, cost efficiency, and reliability (Willsky, 1976; Basseville, 1988). Existing FDD approaches can be roughly divided into two major categories including model-based and knowledge-based approaches (Venkatasubramanian et al., 2003a; Venkatasubramanian et al., 2003b). Model-based approaches make use of the quantitative analytical model of a physical system. Knowledgebased approaches do not need full analytical modeling and allow one to use qualitative models based on the available information and knowledge of a physical system. Whenever the mathematical models describing the system are available, analytical model-based methods are preferred because they are more amenable to performance analysis. Generally, there are two steps in the procedure of model-based FDD. First, on the basis of the available observations and a mathematical model of the system, the state variable x and test statistics are required to be obtained. Then, based on the generated test statistics, it is required to decide on the potential occurrence of a fault. For linear and Gaussian systems, the Kalman filter (KF) is known to be optimal and employed for state estimation. The innovations from the KF are used as the test statistics, based on which hypothesis tests can be carried out for fault detection (Belcastro & Weinstein, 2002). In reality, however, the models representing the evolution of the system and the noise in observations typically exhibit complex nonlinearity and non-Gaussian distributions, thus precluding analytical solution. One popular strategy for estimating the state of such a system as a set of observations becomes available online is to use sequential Monte-Carlo (SMC) methods, also known as particle filters (PFs) (Doucet et al., 2001). These methods allow for a complete representation of the posterior probability distribution function (PDF) of the states by particles (Guo & Wang, 2004; Li & Kadirkamanathan, 2001). The aforementioned FDD strategies are single-model-based. However, a single-model-based FDD approach is not adequate to handle complex failure scenarios. One way to treat this

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. The multiple fault problems is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. In this work, we present an approach for diagnosing multi faults based on model using techniques of detection and localization. We use an observer to generate residuals for a decision in a stage of monitoring and diagnostic system when disruptions or defects occur. Our contribution is the proposal of a diagnostic method when multiple faults type actuators or sensors affect the system.

A formal specification for analysing and implementing multiple fault diagnosis software is proposed in this paper. The specification computes all potential fault sources that correspond to a set of triggered alarms for a safety-related system, or part of a system. The detection of faults occurring in a safety-related system is a fundamental function that needs to be addressed efficiently. Safety monitors for fault diagnosis have been extensively studied in areas such as aircraft systems and chemical industries. With the introduction of intelligent sensors, diagnosis results are made available to monitoring systems and operators. For complex systems composed of thousands of components and sensors, the diagnosis of multiple faults and the computational burden of processing test results are substantial. This paper addresses the multiple fault diagnosis problem for zero-time propagation using a fault propagation graph. Components represented as nodes in a fault propagation graph are allocated with alarms. When faults occur and are propagated some of these alarms are triggered. The allocation of alarms to nodes is based on a severity analysis performed using a form of failure mode and effect analysis on components in the system.

The expert system for fault diagnosis based on classified hierarchical model is addressed so as to improve the maintenance capability of engineering vehicles under complex battle environment. The analysis of fault feature, fault pattern and failure mechanism of the whole machine and its electrical system is accomplished. Furthermore, the structure of classified hierarchical diagnosis model and method of knowledge representation are implemented so that the knowledge base of the fault diagnosis system can be developed. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work effectively increases the fault maintenance, diagnosis and support capabilities of the military troops.

Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. This paper develops near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. The DMFD problem is to determine the most likely evolution of component states, the one that best explains the observed test outcomes. Here, we discuss four formulations of the DMFD problem. These include the deterministic situation corresponding to perfectly observed coupled Markov decision processes to several partially observed factorial hidden Markov models ranging from the case where the imperfect test outcomes are functions of tests only to the case where the test outcomes are functions of faults and tests, as well as the case where the false alarms are associated with the nominal (fault free) case only. All these formulations are intractable NP-hard combinatorial optimization problems. Our solution scheme can be viewed as a two-level coordinated solution framework for the DMFD problem. At the top (coordination) level, we update the Lagrange multipliers (coordination variables, dual variables) using the subgradient method. At the bottom level, we use a dynamic programming technique (specifically, the Viterbi decoding or Max-sum algorithm) to solve each of the subproblems, one for each component state sequence. The key advantage of our approach is that it provides an approximate duality gap, which is a measure of the suboptimality of the DMFD solution. Computational results on real-world problems are presented. A detailed performance analysis of the proposed algorithm is also discussed.

In this paper we present and compare different fault diagnosis algorithms using space space models for nonlinear dynamic systems. Most fault diagnosis and isolation algorithms for dynamic systems, which can be modeled using a set of state space equations, have relied on the system being linear and the noise and disturbances being Gaussian. In such cases, optimal filtering ideas based on Kalman filtering are utilized in estimation followed by a residual analysis, for which whiteness tests are typically carried out. Linearized approximations (e.g., Extended Kalman filters) have been used in the nonlinear dynamic systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. The particle filter is combined with the innovation-based fault detection techniques to develop a fault detection and isolation scheme. The advantage of particle filters is that they are capable of handling any functional nonlinearity and system or measurement noise of any distribution. An improvement on using a single Extended Kalman filter matched to a particular model is to use the the Interacting Multiple Model estimator, which consists of a number of EKFs running in parallel. Such a multiple model estimator can handle the abrupt changes in the system dynamics, which is essential for fault diagnosis. Here, we compare the fault detection performances of these algorithms on different nonlinear systems.

This paper deals with multiple soft fault diagnosis of nonlinear analog circuits and offers a method that allows locating the faulty parameters and evaluating their values. The method works with a system of nonlinear algebraic test equations, which are not given in explicit analytical form, and actually may possess multiple solutions. The solutions are specified by one or several sets of the diagnostic parameter values that meet the test. To find the multiple solutions, an extended systematic search method has been developed. The diagnostic algorithm proposed in this paper exploits this method and brings a new concept for finding the actual solution. As a result, the diagnostic process is considerably improved. It can be applied to bipolar junction transistor and CMOS circuits manufactured in micrometer and nanometer technologies. In the last case, however, the transistors are characterized by intricate models (PSP 103 or BSIM 4). In consequence, the CPU time increases due to very complex sensitivity analyses of the circuits required by the diagnostic method. In such a case, the proposed approach is useful at the preproduction stage, where the CPU time is offline and not crucial. For illustration, two numerical examples are given.

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection, and complex rulemaking, this article proposes a multimodal deep residual filter network (DRFN) for online multiple open-switch fault diagnosis of T-type three-level inverters. It contains low-rank matrix fusion (LMF), a DRFN, and a cross-transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. Then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism outputs the fault state of the T-type three-level inverter. The datasets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis is 99.18%, and the average open-circuit fault diagnosis time is 21 ms.

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.