Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks
Introduction
Both current technological advances and the increasing demand for more productive and reliable industrial processes have resulted in more complex automation with greater data availability. As a result, there is an increasing need for more efficient supervision and control systems, especially for Fault Detection and Diagnosis (FDD) in dynamic environments. In a production system, a fault is an abnormal operating condition caused by factors such as design errors, installation errors, misuse or the effects of natural degradation. The availability of mechanisms for early and reliable detection of faults decreases the risks of malfunction or unscheduled shutdowns of the system. Consequently, it increases equipment reliability and avoids material losses, environmental accidents and harm to workers (Blázquez and Miguel, 2005, Chiang et al., 2001, Romano and Kinnaert, 2006, Yang and Liu, 1998).
Considering the a priori knowledge used for their development, FDD systems are designed using process models or historical data. Such approaches can be subdivided into quantitative and qualitative methods (Venkatasubramanian et al., 2003, Venkatasubramanian et al., 2003). Systems based on historical data can extract relevant features from the data in order to map the relationships and the existing limits between the considered classes or groups. In this group, some quantitative methods can be highlighted: statistical classifiers (Luo et al., 2011, Ma et al., 2010, Soares and Galvão, 2010); neural networks (Lau et al., 2013, Leite et al., 2012, Sartori, 2012, Zarei, 2012); fuzzy logic (Andrade, 2012, Andrade et al., 2011, Lau et al., 2013, Leite et al., 2012, Li et al., 2013, Silva et al., 2012); Principal Component Analysis (PCA) (Barragan et al., 2016, Jiang et al., 2013, Lau et al., 2013); method of partial least squares (Zhang, Zhou, Qin, & Chai, 2010); wavelet transforms (Barragan et al., 2016). Among the qualitative methods, expert systems (Saravanan et al., 2009, Wang et al., 2012, Zadeh, 2008) and qualitative trend analysis (Maurya, Rengaswamy, & Venkatasubramanian, 2007) are noteworthy. In the model-based approaches, the actual behavior of the monitored system is compared to the response obtained by a representative model of the process. The result of this supervised comparison is a residual vector used to detect the presence of faults. In this group the following quantitative methods can be highlighted: state and output observers (Chetouani, 2008, Kalman, 1960); parity space and equations (Beckerle et al., 2012, Blesa et al., 2014, Zakharov et al., 2013, Zhong et al., 2015); extended Kalman filter (Kalman, 1960, Patwardhan and Shah, 2006); support vector machine (Zhang, Zhou, Guo, Zou, & Huang, 2012; Deng et al., 2011, Duan et al., 2016, Park et al., 2011); and parameter identification and estimation methods (Johansson et al., 2006, Pouliezos et al., 1989). In the qualitative approach, the following methods stand out: fault trees (Nguyen and Lee, 2008, Simões Filho, 2006); qualitative simulation (Berleant, 1991); qualitative process theory (Venkatasubramanian, Rengaswamy, & Kavuri, 2003); and Bayesian networks and other Bayesian reasoning extensions, such as signed directed graphs and evidence theory (Ji et al., 2015, Luo et al., 2012, Xiao et al., 2014). Serdio et al., 2014, Serdio et al., 2015) have developed approaches that combine historical data, automatic model extraction and residual generation, which seem useful for fault detection and isolation with abrupt and incipient faults.
Many studies have shown the positive advantages of statistically based and artificial intelligence based techniques for solving FDD problems, especially hybrid systems involving fuzzy logic, genetic algorithms and mainly neural networks (Chen and Chen, 2011, Ghate and Dudul, 2010, Mendonça et al., 2009, Niaki and Abbasi, 2005, Sartori et al., 2012, Sharma et al., 2015, White and Lakany, 2008, Wu, 2011, Zarei, 2012). The noise tolerance shown by artificial neural networks (Özyurt & Kandel, 1996) and the ability of fuzzy logic to deal with information inaccuracies, ambiguities and uncertainties (Leite et al., 2012) mean that neuro-fuzzy hybrid systems are widely used (Hell et al., 2008, Lau et al., 2013). These techniques are well suited to non-linear systems because they do not require explicit mathematical models (Angelov and Yager, 2012, Bartyś et al., 2006, Bocaniala and da Costa, 2006, Li et al., 2013, Lo et al., 2009, Ma et al., 2010, Rigatos and Zhang, 2009). According to Sartori, Amaro, Arduini, Souza Júnior, and Embiruçu (2016), most applications of FDD systems are in power generating and distribution units, pieces of equipment in process industries (reactors, columns, sensors and actuators) and motors and bearings. This study lists neural networks, fuzzy logic, principal component analysis, Kalman filter, support vector machines, genetic algorithms and expert systems as the most commonly used techniques in descending order.
In general, such approaches are well suited to the detection and diagnosis of abrupt faults. In the context of incipient faults in dynamical systems, the FDD problem is more complex and the studies and research found in the literature have yet to find appropriate solutions for problems such as: multivariate problems and a diversity in the number of classes; nonlinear processes with incipient faults present in two or more considered classes; difficulty in obtaining historical data with real-world applications. The main contribution of this paper is to propose a detection and diagnosis system for incipient faults in dynamic systems, without the need to use a mathematical model or residual calculations and with a low false alarm rate. The proposed system is based on Weightless Neural Networks [WNN, an acronysm also used for Wavelet Neural Networks (Lei, He, & Zi, 2011)], initially proposed by Aleksander (1967). WNN are digital models based on Random Access Memory (RAM) devices. Unlike conventional neural models, learning happens in memories inserted into the neuron, in the form of truth tables. Compared to weighted models, WNN have the advantages of a diversity of use of these memories, such as similarity with the conventional digital systems; fast and flexible learning algorithms; accuracy and consistency in the results, no need for generating residuals and network retraining; and above all, great potential for pattern recognition and classification.
The applications of WNN for pattern recognition and classification problems can be found in several areas, including: digit and fingerprint recognition (Bandeira et al., 2009, Conti et al., 2009, Grieco et al., 2010); faces and facial features recognition (Araújo, 2011, Sirlantzis et al., 2009, Subhashini and Nagarajan, 2014); robot navigation (McElroy and Howells, 2011, Nurmaini et al., 2009); data stream clustering (Cardoso, Lima, de Gregório, Gama, & França, 2011); and time series forecasting (De Souza et al., 2010, Mpofu, 2006). However, no work addressing the problems of FDD with the use of WNN was found in the literature. The papers presented by De Gregorio and Giordano (2014) and Cardoso, De Gregorio, Lima, Gama, and França (2012) approach this context, but do not deal with FDD problems. De Gregorio and Giordano (2014) used WNN for the problem of detecting changes in the vision field of a camera. The proposed system, called CwisarDH, uses a discriminator for each coverage point of the video with the color concept. Cardoso et al. (2012) presented the StreamWiSARD system for flow data grouping with sliding-window. The system consists of WiSARD discriminators as primary units and is able to define high quality clusters, restricted to a small number of microclusters. The absence of FDD systems based on WNN, and the potential of these networks for pattern recognition and classification, are the main factors that motivated this work.
In order to validate the developed systems two case studies were considered. The first one uses a univariate database of temperature sensors at a Natural Gas Processing Unit (NGPU) sales gas compressor from Petrobras, located in Pojuca, Bahia, Brazil (Andrade, 2012, Andrade et al., 2011, Sartori, 2012). In the second case study, multivariate data from an industrial plant simulator considered a benchmark in Fault Detection and Diagnosis problems, known as Tennessee Eastman Process (TEP) was used (Downs and Vogel, 1993, Ricker, 1995). In the multivariate application an algorithm of attributes selection per class proposed by Vale, Neto, and Canuto (2010) was used, with modifications proposed in this paper.
This paper is organized as follows: Section 2 presents the fundamentals of WNN; Section 3 describes the FDD-WiSARD (Wilkes-Stonham-Aleksander Recognition Device) system and the method used for training and testing; and in Section 4 the two case studies are used and their respective results are presented. Finally, Section 5 presents concluding remarks.
Section snippets
Weightless Neural Networks
Aleksander (1967), one of the pioneers in the field of ANN (Artificial Neural Networks), proposed an entirely digital neuron/node model based on RAM devices. The model gave rise to a class of neurons based on Boolean logic, also known as weightless neurons or RAM-based neurons. Neural networks made up of these neurons are called Weightless Neural Networks (WNN). The learning in these networks consists of changes in the memory contents in truth tables inserted in the RAM. A weightless node
System structure
The system proposed in this work performs fault detection as a classification problem in which an input pattern x = [a1, a2, …, an], defined as a n-dimensional vector where ai are the measured variables (attributes), is considered a normal operation or fault state. The diagnosis, on the other hand, consists of the partition of the fault state into subclass labels defined by the different types of faults considered. For this, the attributes a1, a2, …, an should contain information that enables the
Natural gas processing unit
For this case study a univariate database with temperature measurements obtained from an industrial plant was used. Temperature sensors are coupled to a Sales Gas Compressor (SGC) in a Natural Gas Processing Unit (NGPU) at Petrobras, located in the town of Pojuca, Bahia state, Brazil (Andrade, 2012, Andrade et al., 2011, Sartori, 2012). This compressor works with a minimum load of 50% and its functional structure includes an electric motor, a crankcase, five compression cylinders, two intake
Conclusions
This work addresses the problem of Fault Detection and Diagnosis in dynamic systems with the use of weightless neural networks. The DW network was used as reference for the development of the proposed systems and these systems were tested and validated with univariate data, representing a real problem (historical data from a NGPU, focusing on the temperature monitoring of its sales gas compressor), and with simulated multivariate data from the so-called TEP benchmark problem. With the results
Acknowledgments
The authors acknowledge CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and FAPESB (Fundação de Amparo à Pesquisa do Estado da Bahia) for their financial support, and thank Jaime Freire de Souza for his collaboration in the development of the implemented software systems.
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