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Entropy Indices Based Fault Detection
Procedia Manufacturing, 2020
Signal processing-based fault diagnosis is a growing domain in control engineering. As a statistical measure, entropy can measure the complexity of signals, this could be strongly related to the functional status of a system which provides these signals. Therefore, entropy can be a promising non-parametric tool to extract different characteristics of manufacturing system provided signals. Recently, many studies have applied entropy indices in diagnosis, detection and prediction of faults. This paper proposes a theoretical approach to investigate the applicability of entropy indices for the fault characteristics extraction from discrete signals. The study uses synthetic test signals of various structures and properties. At first probability density functions are estimated and entropy indices as the Renyi entropy and sample entropy for different lengths are computed. These are compared and put in relation with fault occurrence. The results show that these indices can be a promising no...
A Maximum Entropy Based Approach to Fault Diagnosis Using Discrete and Continuous Features
This paper presents a new maximum entropy (ME) based hybrid inference engine to improve the accuracy of diagnostic decisions using mixed continuous-discrete variables. By fusing the complementary fault information provided by discrete and continuous fault features, false alarms due to misclassification and modeling uncertainty can be significantly reduced. Simulation results using a three-tank benchmark system have clearly illustrated the advantages of diagnostics based on mixed continuous-discrete variables. Moreover, in the presence of significant measurement noise, simulation results show that the proposed ME method achieves better performance than the support vector machine classifier.
Information Based Fault Diagnosis
Proceedings of the 17th IFAC World Congress, 2008, 2008
Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These inputs are disturbance inputs, reference inputs and auxiliary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults.
Enhanced diagnostic certainty using information entropy theory
Advanced Engineering Informatics, 2003
As is well known, the information entropy is a basic notion in cybernetics. This paper mainly summarizes some applications of information entropy in our machinery diagnostic research practice. First, we use it as a quantitative measure of equipment diagnostibility, maintainability. Second, we introduce a criterion of complexity in frequency domain to evaluate the complexity of diagnostic signal in rotating machinery. Then a new criterion called the index of orbit complexity is proposed to evaluate the dynamic quality of rotor systems during their operation. Finally the entropy distance is applied as an effective diagnostic feature to discriminate the potential faults inside the operating machinery. Practical case studies and experiments show its effectiveness.
2004
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Approximate Entropy as a diagnostic tool for machine health monitoring
Mechanical Systems and Signal Processing, 2007
This paper presents a new approach to machine health monitoring based on the Approximate Entropy (ApEn), which is a statistical measure that quantifies the regularity of a time series, such as vibration signals measured from an electrical motor or a rolling bearing. As the working condition of a machine system deteriorates due to the initiation and/or progression of structural defects, the number of frequency components contained in the vibration signal will increase, resulting in a decrease in its regularity and an increase in its corresponding ApEn value. After introducing the theoretical framework, numerical simulation of an analytic signal is presented that establishes a quantitative relationship between the severity of signal degradation and the ApEn values. The results of the simulation are then verified experimentally, through vibration measurement on a realistic bearing test bed. The study has shown that ApEn can effectively characterise the severity of structural defect, with good computational efficiency and high robustness. r
Information theoretic fault detection
Proceedings of the 2005, American Control Conference, 2005., 2005
In this paper we propose a novel method of fault detection based on a clustering algorithm developed in the information theoretic framework. A mathematical formulation for a multi-input multi-output (MIMO) system is developed to identify the most informative signals for the fault detection using mutual information (MI) as the measure of correlation among various measurements on the system. This is a modelindependent approach for the fault detection. The effectiveness of the proposed method is successfully demonstrated by employing MI-based algorithm to isolate various faults in 16cylinder diesel engine in the form of distinct clusters.
Unified approach to the problem of fault diagnosis
2010 Conference on Control and Fault-Tolerant Systems (SysTol), 2010
A unified approach to the problem of fault diagnosis is considered for the cases of continuous-time and discrete-time nonlinear and linear system models as well as for discrete-event model. The feature of this approach is that it allows obtaining the unique on the algorithmic level solution of the problem for all above types of the models. Conventional differential geometric and pair algebra of partitions tools are applied as auxiliary ones for providing the calculations in special model cases.
Fault diagnosis in physical systems turn out to become very complex as soon as the considered systems are no longer elementary and become more and more complex and sophisticated, it is then legitimate for companies to acquire an effective diagnosis system. In our research work, we are interested in the diagnosis of industrial hybrid systems, which are composed of continuous systems, discrete event systems and an interface that manages the interactions between the two aspects. The aim of this paper is, first, to give an overview of hybrid systems, and second, to review the state of the art of methods and techniques for fault diagnosis of hybrid systems.