Fault Diagnosis In Industrial Systems Research Articles

Fault diagnosis methods based on a time-series convolution and the comparison of multiple methods

Valves and other actuators may fail and cause economic losses or safety accidents. To ensure the stable operation of a control system, it is necessary to identify the failures of various valves and carry out the corresponding maintenance. Several methods are designed and implemented for valve fault diagnosis in this paper. In particular, a novel fault diagnosis method based on a time-series convolution network (FDM-TSCN) is proposed, which is built on a time-series data feature extracting and convolutional neural network. FDM-TSCN can classify 18 out of 19 types of fault, while many other methods cannot. This algorithm is presented in detail and implemented as a prototype system. Comprehensive simulations are performed on valve fault datasets that are generated by the development and application of methods for actuator fault diagnosis in industrial systems (DAMADICS). The simulation results prove the effectiveness and superiority of the proposed FDM-TSCN method. All of the source codes and related data in the paper are made available, which enables other researchers to verify the work easily and may inspire them to carry out more informed research.

A Weakly Supervised Learning-Based Oversampling Framework for Class-Imbalanced Fault Diagnosis

With the lack of failure data, class imbalance has become a common challenge in the fault diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced problem by generating the minority samples to balance the training set. However, one of the main challenges of the existing oversampling methods is how to generate high-quality minority samples. Traditional oversampling methods regard all synthetic samples as minority ones to be added to the training set without filtering. The low-quality synthetic samples would distort the distribution of the dataset and worsen the classification performance. In this article, we propose a weakly supervised oversampling method that treats all synthetic samples as unlabeled samples and develops a graph semisupervised learning algorithm to select high-quality synthetic samples, adding into the final training set as minority samples. To improve the quality of synthetic samples, we propose a cost-sensitive neighborhood component analysis dimensionality reduction method to enhance domain information validity in high-dimensional datasets. Finally, combining a boosting-based ensemble framework, we propose a new imbalanced learning framework suitable for high dimensionality and highly imbalanced fault diagnosis in industrial systems. The experimental validation is performed on five real-world wind turbine blade cracking failure datasets and compared to 15 benchmark methods. The experimental results show that average performances and robustness of the proposed framework are significantly better than those of the benchmark methods.

Lamb Wave Based Structural Damage Detection Using Stationarity Tests.

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

A Bi-Level Nested Sparse Optimization for Adaptive Mechanical Fault Feature Detection

Denoising is a permanent topic and there are various denoisers proposed in the fault diagnosis of industrial systems. However, it is still ambiguous to evaluate their performance quantitatively in terms of mean square error (MSE) and further achieve their maximum gains, because it is always infeasible to obtain the MSE metric without real feature signals in the engineering practices. Therefore, leveraging Stein Unbiased Risk Estimator (SURE) theory, a bi-level nested sparse optimization framework (BiNSOF) is proposed to jointly optimize a parameterized sparse denoiser as well as its regularization parameter, further obtaining the near-optimal fault features with a minimum MSE. The inner level of BiNSOF utilizes a $\ell _{1}$ regularized sparse denoiser to describe the intrinsic sparse structure of feature information, which can be effectively addressed by popular primal-dual splitting schemes. The core of the outer optimization level is a SURE-based unbiased estimator for MSE, and the minimum MSE search problem is transformed into a quadratic optimization problem which could be fast solved by classic golden section search schemes. The proposed BiNOSP can perfectly approximate the oracle MSE without any real feature information, and further provides a reliable way to obtain the optimal hyper-parameter sets for the maximum performance gains of the sparse denoiser. The computational complexity of the advocated approach is also investigated. Moreover, its feasibility and performances are profoundly evaluated by a set of comprehensive numerical studies. Lastly, two bearing fault detection cases confirm the applicability and superiority of the proposed framework.

Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems

Currently, industry needs more robust fault diagnosis systems. One way to achieve this is to complement these systems with preprocessing modules. This makes possible to reduce the dimension of the workspace by removing irrelevant information that hides faults in development or overloads the system’s management. In this paper, a comparison between five performance measures in the adjustment of a Gaussian kernel used with the preprocessing techniques: Kernel Fisher Discriminant Analysis (KFDA) and Kernel Principal Component Analysis (KPCA) is made. The measures of performance used were: Target alignment, Alpha, Beta, Gamma and Fisher. The best results were obtained using the KFDA with the Alpha metric achieving a significant reduction in the dimension of the workspace and a high accuracy in the fault diagnosis. As fault classifier in the Tennessee Eastman Process benchmark an Artificial Neural Network was used.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied “chaining” inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation

This paper proposes an approach for Fault Diagnosis and Isolation (FDI) on industrial systems via faults estimation. FDI is presented as an optimization problem and it is solved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. Also, is presented a study of the influence of some parameters from PSO and ACO in the desirable characteristics of FDI, i.e. robustness and sensitivity. As a consequence, the Particle Swarm Optimization with Memory (PSO-M) algorithm, a new variant of PSO was developed. PSO-M has the objective of reducing the number of iterations/generations that PSO needs to execute in order to provide a reasonable quality diagnosis. The proposed approach is tested using simulated data from a DC Motor benchmark. The results and analysis indicate the suitability of the approach as well as the PSO-M algorithm.

Fault diagnosis in industrial systems based on blind source separation techniques using one single vibration sensor

In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. The aim of this paper is to propose an extension of fault detection techniques that may be used when a reduced set of sensors or even one single sensor is available. Fault detection techniques considered here are based on output-only methods coming from the Blind Source Separation (BSS) family, namely Principal Component Analysis (PCA) and Second Order Blind Identification (SOBI). The advantages of PCA or SOBI rely on their rapidity of use and their reliability. Based on these methods, subspace identification may be performed by using the concept of block Hankel matrices which make possible the use of only one single measurement signal. Thus, the problem of fault detection in mechanical systems can be solved by using subspaces built from active principal components or modal vectors. It consists in comparing subspace features between the reference (undamaged) state and a current state. The angular coherence between subspaces is a good indicator of a dynamic change in the system due to the occurrence of faults or damages. The robustness of the methods is illustrated on industrial examples.

A proposal to fault diagnosis in industrial systems using bio-inspired strategies

In this work a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems is presented. The principal aim is to establish a basis for the development of new and viable model-based Fault Diagnosis Methods which improve some difficulties that the current methods cannot avoid. These difficulties are related mainly with fault sensitivity and robustness to external disturbances. In this study, there have been considered the Differential Evolution and the Ant Colony Optimization algorithms. This application is illustrated using simulated data of the Two tanks system benchmark. In order to analyze the advantages of these algorithms to obtain a diagnosis which needs to be sensitive to faults and robust to external disturbances, some experiments with incipient faults and noisy data have been simulated. The results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for Fault Diagnosis.

Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with conditional Gaussian network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.

Diagnostic décentralisé des SED Application aux systèmes manufacturiers

Fault diagnosis of industrial systems is defined as the operation of faults detection and isolation. This paper presents a decentralized approach to realize the diagnosis of Discrete Event Systems (DES). This approach uses a set of diagnosers. Each diagnoser observes a part of the process and takes a local decision about the occurrence of a fault and its localisation. The construction of the local diagnosers is based on a modular modelling of the plant elements, the controller specifications and the temporal information about the actuators reactivity. In order to verify that the set of local diagnosers are capable to diagnose a set of faults within a bounded delay, a notion of co-diagnosability must be defined. All local diagnosis decisions must be merged in order to obtain one global diagnosis decision. This fusion can be realized by a coordinator. An example of manufacturing application is used to illustrate our approach.

A Probabilistic Approach to Fault Diagnosis of Industrial Systems

A method for fault diagnosis of industrial systems is presented. Plant devices, sensors, actuators and diagnostic tests are described as stochastic finite-state machines. A formal composition rule of these models is given to obtain: 1) the set of admissible fault signatures; 2) their conditional probability given any fault; and 3) the conditional probability of a fault given a prescribed signature. The modularity and flexibility of this method make it suitable to deal with complex systems made by a large number of components. The method is used in an industrial automotive application, specifically the diagnosis of the throttle body and of the angular sensors measuring the throttle plate angle is described in detail.

Fault Diagnosis on Industrial Systems Based on a Multiple Model Approach

In this paper, fault diagnosis of industrial systems based on an adaptive filter is developed. Based on the assumption that dynamic behavior of the process is denned as a multiple model approach established around different operating points, a set of residual vector insensitive to faults is established and thus generating a global model exploited in an adaptive filter. The proposed method makes possible fault detection, isolation and estimation for the whole operating range of industrial system. Convergence and stability conditions of the adaptive filter are developed and its performances are tested in simulation. Copyright © 2004 IFAC

Applications of electrical tomography for gas–solids and liquid–solids flows — a review

This paper presents a review of electrical tomography methods for investigating, monitoring and controlling gas–solids and liquid–solids systems. The physical laws governing the electrical measurements and issues associated with image reconstruction are described in some detail. Experimental results, obtained for a number of case studies conducted in the pilot plant scale and industrial rigs, are presented. These include circulating fluidised bed, pneumatic and hydraulic conveyor, multiphase flow metering and hydrocyclone flow. Instantaneous images, captured with the speed up to 200 frames per second, illustrate how flow patterns vary, and reveal the dynamic behaviour of two-phase systems. Application of electrical tomography for control and fault diagnosis in industrial systems is addressed — the examples include dense pneumatic conveying and hydrocyclone performance.