An integrated approach to machine fault diagnosis (original) (raw)
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Fuzzy Neural Network for the Machine Health Diagnosis
Large-scale and complex mechanical equipments usually operate under complicated and terrible conditions, making them inevitable for faults with various modes and severity. As they can incur substantial production loss and recovery cost. It is therefore, extremely essential to prognose an incipient fault before it leads to serious damage. However, faults of large scale and complex mechanical equipments are characterized by weak response, multi-fault coupling, etc., and it is hard to detect and diagnose incipient and compound faults for these equipments. Hence, the need to automatically analyze the data is apparent and provides an opportunity for computational intelligence (CI) methods to have a significant impact on fault diagnosis research. Neural Networks and Fuzzy Logic, individually or combined, can of great support in these studies. Companionship of Artificial Neural Networks (ANN) and Fuzzy logic (FL) have attracted the growing interest of researchers in various scientific and ...
A New Approach for Industrial Diagnosis by Neuro-Fuzzy systems: Application to Manufacturing System
2011
In this study we propose a strategy for the follow -up of the process behavior and detection of failur es. An approach of industrial diagnosis based on the statistical pattern recognition NeuroFuzzy being based on a digital representation and s ymbolic system of the forms is implemented. Within this framework, data-processin g i teractive software of simulation baptized NEFDI AG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted pr imarily to creation, training and test of a classif ication Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three laye rs used to classify patterns and failures.The system selected is the workshop of SCIMAT clinke r , cement factory in Algeria.
New informative features for fault diagnosis of industrial systems by supervised classification
18th Mediterranean Conference on Control and Automation, MED'10, 2010
The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classification task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the normal operating conditions. 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. We show on this example that the addition of these new features allows to decrease the misclassification rate.
1993
The success of unattended manufacturing depends largely on control mechanisms that monitor the current machining state and take means in case of disturbances. Direct methods generally lack on-line capability, whereas indirect methods are difficult to pursue because changes in cutting conditions influence such methods. Knowledge about these changes exists but it is unstructured from the sensor's point of view. In the context of this unstructured knowledge, a hybrid architecture, featuring fuzzy logic and neural networks, is described that copes with the shortcomings of the traditional methods to monitor and diagnose an unattended milling machine. Force, spindle current, and acoustic emission data that were stored in previous experiments are used as input to the neural network after they undergo some signal processing to calculate the membership functions of fuzzy relations. Afterwards, fuzzy logic principles are used to diagnose the system's status with regard to tool wear and chatter. It is shown that the system works reliably in a wide range of operations, correctly renders the current state, and is a viable alternative to existing monitoring methods.
Special issue: Data-driven fault diagnosis of industrial systems
Information Sciences, 2014
Special issue: Data-driven fault diagnosis of industrial systems Fault diagnosis (FD) for engineering systems and components has been attracting considerable attention from researchers and engineers in many engineering areas. With the rapid development of Computational Intelligence techniques such as neural networks, fuzzy logic and evolutionary computation, the studies and applications of model-free FD systems have been favourably advanced in the past decades. Such intelligent FD systems have thoroughly detailed the signals and systems methods to describe the dynamics of failure mechanisms in materials, structures, and rotating equipment or even a complex computer program. In the past decades, although model-free FD techniques have been broadly explored in practice, there are still many challenging issues to be further explored in the design of diagnosis systems and algorithms, performance improvement, fault predictability, robust diagnosis of uncertain systems, and framework development for engineering systems. Some new challenges in designing FD systems are also emerging. Some of them are associated with data mining applications, for instance, rule generation and adaptation for rule-based FD expert systems while the discovery of new faults is realized through clustering algorithms and association analysis. This special issue aims to promote the recent advances of data-driven FD systems and algorithms, and address some challenges in designing and developing the state-of-the-art FD systems and algorithms. As a specific application of computing techniques in industrial systems, this special issue has received considerable attention and support from academics, researchers, and engineers. After extensive reviews and revisions, fourteen papers have been accepted. These papers cover a wide variety of applications of fault diagnosis techniques in process industries, for instance, aircraft jet engine and wind turbine diagnosis, case-base reasoning approach for status prediction of a shaft furnace, and multi-sensor data fusion techniques for monitoring system design. It is believed that all papers are of high quality and significance, and such a collection will be useful and valuable to both researchers and engineers. Below, we provide a summary of the papers published in this special issue. The paper entitled ''Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach'', by Z.N. Sadough Vaninia, K. Khorasania, and N. Meskinb, presents a fault detection and isolation scheme for an aircraft jet engine, where a multiple model approach and dynamic neural networks are employed to accomplish this goal. Using residuals obtained by comparing each network output with the measured jet engine output and by invoking a properly selected threshold for each network, reliable criteria are established for detecting and isolating faults in the jet engine components. The paper entitled ''Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis'', by Y.L. He, R. Wang, S. Kwong, and X.Z. Wang, considers a problem of simultaneous fault diagnosis and proposes a new Bayesian classifier for problem solving without assuming the independence among features. Simulation results show that the proposed approach significantly outperforms the traditional ones when the dependence exists among features. The paper entitled ''A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace'', by A.J. Yan, W.X. Wang, C.X. Zhang, and H. Zhao, develops an improved CBR-based fault prediction method for a shaft furnace, where a water-filling theory-based weight allocation model and a group decision-making-based revision model are used for improving the prediction accuracy. Also, the optimal allocation mechanism of channel power and the credibility of historical results are used to enhance the predicted results. The paper entitled ''Improved process monitoring and supervision based on a reliable multi-stage feature-based pattern recognition technique'', by L. Al-Shrouf, M. Saadawia, and D. Soffker, presents two new multi-sensor data fusion algorithms for object detection in monitoring of industrial processes. The goals of this work are to reduce the rate of false detection and obtain reliable decisions on the presence of target objects. In this work, the classifier is trained and validated by using the real industrial data. The two algorithms are also tested by using the same data, and their performance and modelling complexity are compared.
Applied Intelligence, 2018
This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function "PDF" that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach "Variable Learning Rate Gradient Descent with Bayes' Maximum Likelihood formula" VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem.
Neuro-Fuzzy Surveillance for Industrial Process Fault Detection
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.
An effective neuro-fuzzy paradigm for machinery condition health monitoring
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001
An innovative neuro-fuzzy network appropriate for fault detection and classification in a machinery condition health monitoring environment is proposed. The network, called an incremental learning fuzzy neural (ILFN) network, uses localized neurons to represent the distributions of the input space and is trained using a one-pass, on-line, and incremental learning algorithm that is fast and can operate in real time. The ILFN network employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized structure with the ability to adaptively learn new classes of failure modes and update its parameters continuously while monitoring a system. To demonstrate the feasibility and effectiveness of the proposed network, numerical simulations have been performed using some well-known benchmark data sets, such as the Fisher's Iris data and the Deterding vowel data set. Comparison studies with other well-known classifiers were performed and the ILFN network was found competitive with or even superior to many existing classifiers. The ILFN network was applied on the vibration data known as Westland data set collected from a U.S. Navy CH-46E helicopter test stand, in order to assess its efficiency in machinery condition health monitoring. Using a simple fast Fourier transform (FFT) technique for feature extraction, the ILFN network has shown promising results. With various torque levels for training the network, 100% correct classification was achieved for the same torque levels of the test data.
Fault Detection and Diagnosis in Industrial Systems, Based on Fuzzy Logic – a Short Review
2015
The paper presents a fuzzy method in fault detection and diagnosis. This method provides a systematic framework to process vague variables and vague knowledge. The supervision of the process requires the treatment of quantitative and qualitative knowledge. Here fuzzy logic approaches are especially attractive for symptom generation with fuzzy thresholds, linguistically described observed symptoms and the approximate reasoning with multi levele fuzzy rule based systems for fault symptom tree structures.
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.