A Total Least Squares Approach of Pattern Recognition for Model Based Fault Detection (original) (raw)
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Fuzzy Pattern Recognition Based Fault Diagnosis
Proceedings of the Seventh International Conference on Enterprise Information Systems, 2005
In order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process.
Fault detection under fuzzy model uncertainty
International Journal of Automation and Computing, 2007
The paper tackles the problem of robust fault detection using Takagi-Sugeno fuzzy models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such a method is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model design procedure. The bounded-error approach is applied to generate the rules for the model using available measurements. The proposed approach is applied to fault detection in the DC laboratory engine.
Annual Conference of the PHM Society, 2014
Nowadays, in several areas, efficient fault diagnosis methods for complex machinery and equipments are required. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. In general, these methods use mathematical/statistical models, accumulated experience, or even process historical data to perform fault diagnosis. Although methods based on models or experience have shown to be effective, they have the disadvantage of requiring previous knowledge of the dynamic system in question. On the contrary, methods based on process historical data do not require a prior knowledge, they are based solely on data obtained directly from the dynamic system. The application of so-called "Evolving Intelligent Systems" to accomplish fault diagnosis from process data have been shown a promising approach. This paper proposes an evolving fuzzy classifier based on a new approach that combines a recursive clustering algorithm and a drift detection method and its application on dynamic systems fault diagnosis. The novel approach provides greater robustness to outliers and noise present in data from process sensors. The classifier is evaluated in fault diagnosis of an interacting tank system and the results are promising. Maurilio Inacio et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A Fuzzy Decision Tree for Fault Classification
Risk Analysis, 2008
In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.
Fuzzy model-based fault detection and isolation
EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696), 2003
The model-based fault detection and diagnosis (FDI) is an interesting method, because of economical and safety related matters. However, in practice it is very dificult to achieve an accurate modeling for complex nonlinear systems. If the system structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. Fuzzy system theory is an interesting tool to handle these situations. The use of characteristics of fuzzy logic theory makes it suitable for fault diagnosis. In this paper, a simulator of an industrial servo-actuated valve is used to simulate several faults in the system. These faults are some of the possible faults in the real system. The fuzzy model-based FDI system developed in this paper was able to detect and isolate all the simulated faults.
Fault Detection and Isolation Using Hybrid Parameter Estimation and Fuzzy Logic Residual Evaluation
Informatica
Fault diagnosis has become an issue of primary importance in modern process automation as it provides the prerequisites for the task of fault detection. The ability to detect the faults is essential to improve reliability and security of a complex control system. When a physical parameter change due to failure has occurred in a system, the failure effect will hardly be visible in the output performance. Since the failure, effect is reflected as a change in the predictor model. In this paper we describe a completed feasibility study demonstrating the merit of employing hybrid parameter-estimation and fuzzy logic for fault diagnosis. In this scheme, the residual generation is obtained from input-output data process, and identification technique based on ARX model, and the residual evaluation is based on fuzzy logic adaptive threshold method. The proposed fault detection and isolation tool has been tested on a magnetic levitation vehicle system.
Journal of The Franklin Institute-engineering and Applied Mathematics, 2022
Process monitoring and diagnosis are crucial for efficient and optimal operation of a chemical plant. Most multivariate statistical process monitoring strategies, such as principal component analysis, kernel principal component analysis, and dynamic principal component analysis, take advantage of the squared prediction error statistic to monitor the state of samples in a residual subspace (RS). Squared prediction error is defined as the square of the 2-norm of a residual vector, and it is calculated as the squared norm of the residual components. When the distributions of variables in an RS are quite different from one another, the detection ability of squared prediction error visibly declines. To accurately monitor the faults occurring in the RS, a new fault detection index based on a weighted combination of Hotelling's T 2 and squared Euclidean distance is developed in this paper. Principal component analysis is first introduced for dividing the original input space into a principal component subspace and an RS. Next, a weighted and combined index is implemented to monitor the variability of samples in the RS. In addition, a corresponding fault diagnosis strategy based on the contribution plot is also developed in this paper. The proposed method is tested on a numerical example and the Tennessee Eastman process. Simulation results show that the new index is effective in both fault detection and diagnosis. KEYWORDS fault detection, fault diagnosis, principal component analysis, residual subspace, weighted and combined index 1 | INTRODUCTION Statistical process control is a pervasive industrial task with the aim of monitoring the evolution of the overall process operation state. It is implemented mostly by means of control charts that are designed to assess whether the process is only subject to normal causes of variation inherent to its operation or if a special cause of variation has occurred that needs to be detected, diagnosed and fixed or accommodated. 1 With the large number of variables measured and stored automatically by distributed control systems, multivariate statistical process control (MSPC) approaches have been developed and successfully applied to monitor various industrial processes. The majority of the proposed MSPC schemes are focused on the detection of shifts in the process mean. 2-4
A New Fault Diagnosis Method Using Fault Directions in Partial Least Square
2011
In this paper, we propose a new approach of fault detection and diagnosis combining a Neural Nonlinear Principal Component Analysis (NNLPCA) and Partial Least Square (PLS). We have made a comparative study between the Linear Principal Component Analysis (LPCA) and Nonlinear Principal Component Analysis (NLPCA) to monitor a manufacturing process. This study has shown the capability of NLPCA in explaining nonlinear correlations in the process data. The traditional LPCA is limited to complex nonlinear systems; therefore, an adaptive NLPCA based on an improved training auto-associative neural network is presented. The proposed approach is applied to fault detection of a manufacturing process. The performance of the proposed approach is then illustrated and compared to those of classic LPCA. Keywords— Neural Nonlinear Principal Component Analysis, Partial Least Square, Clustering, Pre-analysis, Fault visualization, Fault diagnosis.
This paper presents the implementation of the combination of time-series modeling and nearest neighbor classification method in detecting common faults in rotating machineries. In this paper we propose the utilization of parametric distance as an instrument to diagnose faults. The parametric distance is defined as the Euclidean distance between the vector of parameters of an unknown fault and the vector of parameters of known faults obtained from the learning stage. Since the vectors are defined in a hyperspace spanned by the parameters of the identified time-series model, the parametric distance is definitely metric. The method has been successfully implemented in the laboratory using a simple vibration test rig.
Insight - Non-Destructive Testing and Condition Monitoring, 2009
This paper presents a fault diagnosis method based on a fuzzy inference system (FIS) in combination with decision trees. Experiments were conducted on an external gear hydraulic pump. The vibration signal from a piezoelectric transducer is captured for the following conditions: 'Normal pump' (GOOD), 'Journal-bearing with inner face wear' (BIFW), 'Gear with tooth face wear' (GTFW) and 'Journalbearing with inner face wear and Gear with tooth face wear' (G&BW), for three working levels of pump speed (1000, 1500 and 2000 r/min). The features of signal were extracted using descriptive statistic parameters. The J48 algorithm is used as a feature selection procedure to select pertinent features from the data set. The output of the J48 algorithm is a decision tree that was employed to produce the crisp if-then rule and membership function sets. The structure of the FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500 and 2000 r/min conditions were 100%, 96.42% and 89.28%, respectively. The results indicate that the combined J48-FIS model has the potential for fault diagnosis of hydraulic pumps.