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.
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.
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.
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.
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.
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.