Industrial Monitoring by Evolving Fuzzy Systems

International Journal of Fuzzy Systems and Advanced Applications, 2021

In this paper, our objective is dedicated to the detection of a deterioration in the estimated operating time by giving preventive action before a failure, and the classification of breakdowns after failure by giving the action of the diagnosis and / or maintenance. For this reason, we propose a new Neuro-fuzzy assistance prognosis system based on pattern recognition called "NFPROG" (Neuro Fuzzy Prognosis). NFPROG is an interactive simulation software, developed within the Laboratory of Automation and Production (LAP) -University of Batna, Algeria. It is a four-layer fuzzy preceptor whose architecture is based on Elman neural networks. This system is applied to the cement manufacturing process (cooking process) to the cement manufacturing company of Ain-Touta-Batna, Algeria. And since this company has an installation and configuration S7-400 of Siemens PLC PCS7was chosen as a programming language platform for our system.

2008

In the last decade considerable research efforts have been spent to seek for systematic approaches to Fault Diagnosis (FD) in dynamical systems The problem of fault detection consists in detecting faults in a physical system by monitoring its inputs and outputs This paper presents a methodology to monitor and diagnose machine faults in complex industrial processes using neuro-fuzzy methodology.

Fuzzy clustering allows finding classes through the historical data in order to associate them with functional states useful to represent the complex industrial processes behavior. By means of classes, an automaton can be established that determines the current and the next connections of functional states of a process. When fuzzy clustering is used, the connections in the historical data are considered but it does not find other important connections. To solve this limitation, a new method to seek the most important connections among functional states is proposed. Initially, the approach defines an initial transition degrees matrix, where all connections are taken into account. Through a proposed update step, the most important connections are obtained, which they describe the real behavior of a process. In addition, a new distance criterion is defined to improve the update step. The final transition degrees matrix is used to construct a fuzzy automaton that it is validated by human operator's experience. The approach was tested in a steam generator process. Applying three fuzzy clustering algorithms in case of study, the proposed method finds the same transition matrix. The new connections were validated by the human operator.

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Abstract-In the last decade considerable research efforts have been spent to seek for systematic approaches to Fault Diagnosis (FD) in dynamical systems The problem of fault detection consists in detecting faults in a physical system by monitoring its inputs and ...

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010

A new technique to the design and use of inferential 4 sensors in the process industry is proposed in this paper, which 5 is based on the recently introduced concept of evolving fuzzy 6 models (EFMs). They address the challenge that the modern 7 process industry faces today, namely, to develop such adaptive and 8 self-calibrating online inferential sensors that reduce the mainte-9 nance costs while keeping the high precision and interpretability/ 10 transparency. The proposed new methodology makes possible 11 inferential sensors to recalibrate automatically, which reduces 12 significantly the life-cycle efforts for their maintenance. This is 13 achieved by the adaptive and flexible open-structure EFM used. 14 The novelty of this paper lies in the following: 1) the overall 15 concept of inferential sensors with evolving and self-developing 16 structure from the data streams); 2) the new methodology for 17 online automatic selection of input variables that are most relevant 18 for the prediction; 3) the technique to detect automatically a shift 19 in the data pattern using the age of the clusters (and fuzzy rules); 20 4) the online standardization technique used by the learning pro-21 cedure of the evolving model; and 5) the application of this inno-22 vative approach to several real-life industrial processes from the 23 chemical industry (evolving inferential sensors, namely, eSensors, 24 were used for predicting the chemical properties of different 25 products in The Dow Chemical Company, Freeport, TX). It should 26 be noted, however, that the methodology and conclusions of this 27 paper are valid for the broader area of chemical and process indus-28 tries in general. The results demonstrate that well-interpretable 29 and with-simple-structure inferential sensors can automatically be 30 designed from the data stream in real time, which predict various 31 process variables of interest. The proposed approach can be used 32 as a basis for the development of a new generation of adaptive and 33 evolving inferential sensors that can address the challenges of the 34 modern advanced process industry. 35 Index Terms-Concept shift in data streams, evolving fuzzy 36 systems, fuzzy-rule aging, inferential sensors, learning and adap-37 tation, Takagi-Sugeno (TS) fuzzy models.

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 2012

Reliability of sensor measurement is vital to assure the performance of complex and nonlinear industrial operation. In this paper, the problem of designing and development of a data driven multiple sensor fault detection and isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using a model derived from the available input-output measurement. Our proposed MSFDI algorithm is applied to continuously stirred tank reactor sensor fault detection and isolation. Simulation results demonstrate and validate the performance capabilities of our proposed MSFDI algorithm.

Engineering Applications of Artificial Intelligence, 2007

The paper tackles the problem of robust fault detection using Takagi-Sugeno neuro-fuzzy (N-F) models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such an approach is corrupted by model uncertainty due to the fact that in real applications there exists a modelreality mismatch. In order to ensure reliable fault detection, the adaptive threshold technique is used to deal with the problem. The paper focuses also on the N-F model design procedure. The bounded-error approach is applied to generate rules for the model using available data. The proposed algorithms are applied to fault detection in a valve that is a part of the technical installation at the Lublin sugar factory in Poland. Experimental results are presented in the final part of the paper to confirm the effectiveness of the method.

Expert Systems with Applications, 2015

This study develops an evolving output-context fuzzy system (EOCFS) that initiates the evolving process with a single fuzzy rule in which consequent and antecedent parts cover the whole output and input domains respectively. The EOCFS further evolves the output domain and adds more fuzzy rules to achieve an effective rule base. The proposed model is a self-organizing method that can automatically identify prominent distinct data in the output domain for a new fuzzy rule. Thus, the EOCFS constantly evolves by reducing the model error. In addition, the evolving process is realized on the output domain while the self-adaptive process is achieved on the output domain and its associated input domain. The evolving termination index and uncertainty controller of the self-adaptive process are dynamically attained from past and current knowledge. Therefore, effective rule base is the balanced fuzzy model of the approximated system. To illustrate the effectiveness of the proposed algorithm, synthetic and real-world data are considered with low to high dimension inputs. Results show that the proposed EOCFS achieves better performance than the existing models with regard to accuracy and number of rules.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to clas...

Computers & Chemical Engineering, 2003

In this paper, fault diagnosis based on patterns exhibited in the sensors measuring the process variables is considered. The temporal patterns that a process event leaves on the measured sensors, called event signatures, can be utilized to infer the state of operation using a pattern-matching approach. However, the qualitative nature of the features leads to imprecise classification boundaries at the trend-identification stage and hence at the trend-matching stage. Moreover, noise and other underlying phenomena may lead to non-reproducibility of the same trends chosen to represent an event. Thus, a crisp inference process might lead to a large knowledge-base of signatures; it could also cause misclassification. To overcome this, a fuzzy-reasoning approach is proposed to ensure robustness to the inherent uncertainty in the identified trends and to provide succinct mapping. A two-staged strategy is employed: (i) identifying the most likely fault candidates based on a similarity measure between the observed trends and the event-signatures in the knowledge-base and, (ii) estimation of the fault magnitude. The fuzzy-knowledge-base consists of a set of physically interpretable if Á/then rules providing physical insight into the process. The technique provides multivariate inferencing and is transparent. We illustrate the application of the proposed approach in the fault diagnosis of an exothermic reactor case study.