This paper is devoted to measuring the continuous diagnosis capability of a system. A key metric and its calculation models are proposed enabling us to measure the continuous diagnosis capability of a system directly without establishing and searching the sequential fault tree (SFT) of the system. At first a description of a D matrix is given and its metric is defined to determine the weakness of a continuous diagnosis. Then based on the definition of a sequential fault combination, a sequential fault tree (SFT) is defined with its establishment process summarized. A key SFT metric is established to measure the continuous diagnosis capability of a system. Two basic types of dependency graphical models (DGMs) and one combination type of DGM are selected for characteristics analysis and establishment of metric calculation models. Finally, both the SFT searching method and direct calculation method are applied to two designs of one type of an auxiliary navigation equipment, which shows the high efficiency of the direct calculation method.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
The paper presents a heuristic approach to the problem of analog circuit diagnosis. Different optimization techniques in the field of test point selection are discussed. Two new algorithms: SALTO and COSMO have been introduced. Both searching procedures have been implemented in a form of the expert system in PROLOG language. The proposed methodologies have been exemplified on benchmark circuits. The obtained results have been compared to the others achieved by different approaches in the field and the benefits of the proposed methodology have been emphasized. The inference engine of the heuristic algorithms has been presented and the expert system knowledge-base construction discussed.
The paper deals with a multiple fault diagnosis of DC transistor circuits with limited accessible terminals for measurements. An algorithm for identifying faulty elements and evaluating their parameters is proposed. The method belongs to the category of simulation before test methods. The dictionary is generated on the basis of the families of characteristics expressing voltages at test nodes in terms of circuit parameters. To build the fault dictionary the n-dimensional surfaces are approximated by means of section-wise piecewise-linear functions (SPLF). The faulty parameters are identified using the patterns stored in the fault dictionary, the measured voltages at the test nodes and simple computations. The approach is described in detail for a double and triple fault diagnosis. Two numerical examples illustrate the proposed method.
This article presents combined approach to analog electronic circuits testing by means of evolutionary methods (genetic algorithms) and using some aspects of information theory utilisation and wavelet transformation. Purpose is to find optimal excitation signal, which maximises probability of fault detection and location. This paper focuses on most difficult case where very few (usually only input and output) nodes of integrated circuit under test are available.
The paper deals with multiple soft fault diagnosis of analogue circuits. A method for diagnosis of linear circuits is developed, belonging to the class of the fault verification techniques. The method employs a measurement test performed in the frequency domain, leading to the nonlinear least squares problem. To solve this problem the Powell minimization method is applied. The diagnostic method is adapted to real circumstances, taking into account deviations of fault-free parameters and measurement uncertainty. Two examples of electronic circuits encountered in practice demonstrate that the method is efficient for diagnosis of middle-sized circuits. Although the method is dedicated to linear circuits it can be adapted to multiple soft fault diagnosis of nonlinear ones. It is illustrated by an example of a CMOS circuit designed in a sub-micrometre technology.
This paper deals with multiple soft fault diagnosis of nonlinear analog circuits comprising bipolar transistors characterized by the Ebers-Moll model. Resistances of the circuit and beta forward factor of a transistor are considered as potentially faulty parameters. The proposed diagnostic method exploits a strongly nonlinear set of algebraic type equations, which may possess multiple solutions, and is capable of finding different sets of the parameters values which meet the diagnostic test. The equations are written on the basis of node analysis and include DC voltages measured at accessible nodes, as well as some measured currents. The unknown variables are node voltages and the parameters which are considered as potentially faulty. The number of these parameters is larger than the number of the accessible nodes. To solve the set of equations the block relaxation method is used with different assignments of the variables to the blocks. Next, the solutions are corrected using the Newton-Raphson algorithm. As a result, one or more sets of the parameters values which satisfy the diagnostic test are obtained. The proposed approach is illustrated with a numerical example.
The paper deals with fault diagnosis of nonlinear analogue integrated circuits. Soft spot short defects are analysed taking into account variations of the circuit parameters due to physical imperfections as well as self-heating of the chip. A method enabling to detect, locate and estimate the value of a spot defect has been developed. For this purpose an appropriate objective function was minimized using an optimization procedure based on the Fibonacci method. The proposed approach exploits DC measurements in the test phase, performed at a limited number of accessible points. For illustration three numerical examples are given.
This paper presents a Kalman filter based method for diagnosing both parametric and catastrophic faults in analog circuits. Two major innovations are presented, i.e., the Kalman filter based technique, which can significantly improve the efficiency of diagnosing a fault through an iterative structure, and the Shannon entropy to mitigate the influence of component tolerance. Both these concepts help to achieve higher performance and lower testing cost while maintaining the circuit.s functionality. Our simulations demonstrate that using the Kalman filter based technique leads to good results of fault detection and fault location of analog circuits. Meanwhile, the parasitics, as a result of enhancing accessibility by adding test points, are reduced to minimum, that is, the data used for diagnosis is directly obtained from the system primary output pins in our method. The simulations also show that decision boundaries among faulty circuits have small variations over a wide range of noise-immunity requirements. In addition, experimental results show that the proposed method is superior to the test method based on the subband decomposition combined with coherence function, arisen recently.
This paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.
Current methods of fault diagnosis for the grounding grid using DC or AC are limited in accuracy and cannot be used to identify the locations of the faults. In this study, a new method of fault diagnosis for substation grounding grids is proposed using a square-wave. A frequency model of the grounding system is constructed by analyzing the frequency characteristics of the soil and the grounding conductors into which two different frequency square-wave sources are injected. By analyzing and comparing the corresponding information of the surface potentials of the output signals, the faults of the grounding grid can be diagnosed and located. Our method is verified by software simulation, scale model experiments and field experiments.
Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By identifying combinations of faults in a logical framework it’s possible to define the structure of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic tools is in their ability to reasoning under uncertainty. Some typical constraints to the fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper shows that information processing has become simple and easy through the use of Bayesian networks. The study presented showed that updating knowledge and exploiting new knowledge does not complicate calculations. The contribution is the structural approach of faults diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are defined in descending order. The approach presented in this paper has been successfully applied to turbo compressor, which represent vital equipment in petrochemical plant.
A new soft-fault diagnosis approach for analog circuits with parameter tolerance is proposed in this paper. The approach uses the fuzzy nonlinear programming (FNLP) concept to diagnose an analog circuit under test quantitatively. Node-voltage incremental equations, as constraints of FNLP equation, are built based on the sensitivity analysis. Through evaluating the parameters deviations from the solution of the FNLP equation, it enables us to state whether the actual parameters are within tolerance ranges or some components are faulty. Examples illustrate the proposed approach and show its effectiveness.
The paper deals with the problems of designing observers and unknown input observers for discrete-time Lipschitz non-linear systems. In particular, with the use of the Lyapunov method, three different convergence criteria of the observer are developed. Based on the achieved results, three different design procedures are proposed. Then, it is shown how to extend the proposed approach to the systems with unknown inputs. The final part of the paper presents illustrative examples that confirm the effectiveness of the proposed techniques. The paper also presents a MATLAB® function that implements one of the design procedures.
This paper describes the use of new methods of detecting faults in medium-voltage overhead lines built of covered conductors. The methods mainly address such faults as falling of a conductor, contacting a conductor with a tree branch, or falling a tree branch across three phases of a medium-voltage conductor. These faults cannot be detected by current digital relay protection systems. Therefore, a new system that can detect the above mentioned faults was developed. After having tested its operation, the system has already been implemented to protect mediumvoltage overhead lines built of covered conductors.
While the Slope Fault Model method can solve the soft-fault diagnosis problem in linear analog circuit effectively, the challenging tolerance problem is still unsolved. In this paper, a proposed Normal Quotient Distribution approach was combined with the Slope Fault Model to handle the tolerances problem in soft-fault diagnosis for analog circuit. Firstly, the principle of the Slope Fault Model is presented, and the huge computation of traditional Slope Fault Characteristic set was reduced greatly by the elimination of superfluous features. Several typical tolerance handling methods on the ground of the Slope Fault Model were compared. Then, the approximating distribution function of the Slope Fault Characteristic was deduced and sufficient conditions were given to improve the approximation accuracy. The monotonous and continuous mapping between Normal Quotient Distribution and standard normal distribution was proved. Thus the estimation formulas about the ranges of the Slope Fault Characteristic were deduced. After that, a new test-nodes selection algorithm based on the reduced Slope Fault Characteristic ranges set was designed. Finally, two numerical experiments were done to illustrate the proposed approach and demonstrate its effectiveness.
Wind energy has achieved prominence in renewable energy production. There fore, it is necessary to develop a diagnosis system and fault-tolerant control to protect the system and to prevent unscheduled shutdowns. The presented study aims to provide an experimental analysis of a speed sensor fault by hybrid active fault-tolerant control (AFTC) for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). The hybrid AFTC switches between a traditional controller based on proportional integral (PI) controllers under normal conditions and a robust backstepping controller system without a speed sensor to avoid any deterioration caused by the sensor fault. A sliding mode observer is used to estimate the PMSG rotor position. The proposed controller architecture can be designed for performance and robustness separately. Finally, the proposed methodwas successfully tested in an experimental set up using a dSPACE 1104 platform. In this experimental system, the wind turbine with a generator connection via a mechanical gear is emulated by a PMSM engine with controled speed through a voltage inverter. The obtained experimental results show clearly that the proposed method is able to guarantee service production continuity for the WECS in adequate transition.
Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.
Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.
Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.
Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including datadriven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input - output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.
The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.