Test Point Selection Research Articles

Quantum-inspired evolutionary algorithm for analog test point selection

An important problem that arises in fault diagnosis of analog circuit for fault dictionary technique is the test point selection, which is known to be NP-hard. This paper develops a mathematical optimization model for analog test point selection (ATPS) problem and proposes a novel method to solve it based on quantum-inspired evolutionary algorithm (QEA). The proposed method uses the solution produced by the inclusive algorithm to initialize Q-bit individuals and presents a new fitness function to search the global minimum test point set. In addition, an approach for dynamically determining the magnitude of rotation angle is introduced to accelerate the convergent speed. The efficiency of the proposed algorithm is proven by one practical analog circuit example and a group of statistical experiments. Results show that the proposed algorithm, compared with other methods, finds the global minimum set of test points more efficiently and more accurately.

Study on Method for Test Points Selection under Uncertainty Based on MBQPSO

Test points choosing are the beginning of optimization of design for testability. With the consideration of uncertain influences on the tests of electronic equipment, the model for design for testability was proposed based on hybrid diagnosis modified by Bayesian network. Based on the new model, the algorithm of MBQPSO was proposed, which could take use of multi-dimension searching mechanism to choose test points according to uncertain correlation matrix between failure modes and tests. With the experiment, the result of this proposed method is closer to the reality and can provide better guidance for future design for testability.

Experimental method for the selection of points of measurement diagnostic vibration signals on internal combustion engine

The diagnostic testing of internal combustion engine can be made by using working processes and methods which take advantage of leftover processes. Working processes give information about general condition of internal combustion engine. Leftover processes give information about condition of particular subassemblies and kinematic couples; hence they are used as autonomous processes or as processes supporting other diagnostic methods. Methods based on analysis of vibrations and noise changes to determine technical condition of object are named as vibroacoustic diagnostics. In papers about vibroacoustic diagnostics of engine, problems connected with difficulty to select test point and to define diagnostic parameters containing essential information about engine’s condition, are most often omitted. Selection of engine’s working parameters and conditions of taking measurements or recording vibration signal are usually based on references, researcher’s experience or intuition. General assumptions about taking measurements of signal closest to its source are most often used. This paper presents a new approach to vibroacoustic diagnostics of internal combustion engine. Selection of test points of vibration on the basis of impact tests results was suggested and performed a sensitivity analysis of measurement points on the cylinder head, the force impulse.

Spindle speed selection for tool life testing using Bayesian inference

According to the Taylor tool life equation, tool life is dependent on cutting speed (or spindle speed for a selected tool diameter in milling) and their relationship is quantified empirically using a power law exponent, n, and a constant, C, which are tool-workpiece dependent. However, the Taylor tool life model is deterministic and does not incorporate the inherent uncertainty in tool life. In this work, Bayesian inference is applied to estimate tool life. With this approach, tool life is described using a probability distribution at each spindle speed. Random sample tool life curves are then generated and the probability that a selected curve represents the true tool life curve is updated using experimental results. Tool wear tests are performed using an inserted (uncoated) carbide endmill to machine AISI 1018 steel. The test point selection is based on the maximum value of information approach. The updated beliefs are then used to predict tool life using a probability distribution function.

Housing Decoration Lower Indoor Radon Concentration Survey

Select a residential projects under construction in Chongqing for the study, testing on indoor radon concentration at different decorated stages. This survey found that indoor radon concentration values for the selected test points are lower than the national limits, meet the requirements. Through decoration, decoration materials such as cement mortar, latex paint, wallpaper, flooring and other ways to reduce indoor radon concentration is possible. The other hand, use of porcelain tiles will elevate indoor radon concentration, so you should master its radioactive contents in advance when you select the repair material, choose green materials as possible.

Two Heuristic Algorithms for Test Point Selection in Analog Circuit Diagnoses

Two Heuristic Algorithms for Test Point Selection in Analog Circuit Diagnoses 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.

Methods of Handling the Tolerance and Test-Point Selection Problem for Analog-Circuit Fault Diagnosis

Soft-fault diagnosis and tolerance are two challenging problems in analog-circuit fault diagnosis. Although many analog faults can be diagnosed theoretically, they cannot be accurately diagnosed due to the influence of component tolerance. This paper proposes approaches to the tolerance-handling method in soft-fault diagnosis. First, the slope fault model and its theoretical proof are presented. In linear analog circuits, the voltage equation between two nodes is linear and can be expressed by a point–slope-form equation in which the point is determined by the nominal voltage values on the two selected nodes, and the slope, which is invariant, can be used as the fault model, which is applicable to both hard (open or short) and soft (parametric) faults. Then, the parameter tolerance is taken into consideration. The point–slope-form equation is governed by a point and a slope value. The tolerance influences the fault diagnosis by shifting both the point and the nominal slope value. These influences can be mitigated in two ways: 1) The point is measured when an actual circuit is under steady state and free of fault, and 2) the maximum slope deviation is readily obtained by using optimization theory if the equation governing the voltage between two nodes is available; otherwise, a threshold proportion coefficient is used to calculate the maximum deviation. Based on these methods, a novel test-point selection approach for the fault dictionary technique is proposed. The effectiveness of the proposed approaches is verified by both simulated and experimental results.

Test Points Selection for Analog Fault Diagnosis Using Immune Clonal Selection Algorithm

Test Points Selection for Analog Fault Diagnosis Using Immune Clonal Selection Algorithm

A Novel Test Point Selection Method for Analog Fault Dictionary Techniques

Most of the recently reported test point selection algorithms for analog fault dictionary techniques are based on integer-coded table (ICT) technique. Hence, the accuracy of these algorithms is closely related to the accuracy of the ICT technique. Unfortunately, this technique is not accurate, especially when the size of fault dictionary is large. This paper proposes an accurate fault-pair Boolean table technique for the test point selection problem. First, the approach to transform the fault dictionary into a fault-pair Boolean table is introduced. Then, a test point selection algorithm based on the fault-pair Boolean table is proposed. Thirdly, several example circuits are used to illustrate the proposed algorithm. Simulated results indicate that the proposed method is more accurate than the other methods. Therefore, it is a good solution for minimizing the size of the test point set.

Multidimensional Fitness Function DPSO Algorithm for Analog Test Point Selection

A novel multidimensional fitness function discrete particle swarm optimization algorithm is proposed to optimize analog test point selection. The proposed method uses fault isolation rate and the number of test points to formulate a multidimensional fitness function to search the global minimal test point set, and an elitist set is used to get more than one possible best solution in the described approach. The efficiency of the proposed method is proven by the same experiments used to verify other methods for optimal test points. Results show that the proposed algorithm in this paper cannot only reduce the computation complexity but also shorten the time consumption. It is particularly useful for large-scale analog circuits.

A MOGA-based approach for optimal analogue test points selection

Test point selection is one of the important topics for testability analysis. In this paper, a multi-objective genetic algorithm (MOGA)-based approach for optimal analogue test point selection is proposed. In this described method, redundancy deleting operator is used in Pareto frontier, crowding distances in objective space and decision space are combined, a new crossover operator based on Diff and an adaptive mutation operator is adopted, expecting to improve the exploration of the GA. Then the post-compromise is used to get a compromised optimal solution. Experiments on a practical example of analogue circuit and a series of hypothetical circuits indicate that the proposed method outperforms the other two existing methods, that is, entropy-based method and the GA-based method, in effectiveness and efficiency.

Research on optimizing the fault diagnosis strategy of complex electronic equipment

Diagnosis strategy is a testing sequence of the fault detection and isolation. For the distribution of the electronic equipment, a feasible engineering maintenance method is put forward based on the questions of test point selection and diagnosis strategy. The concepts of local diagnosis strategy and global diagnosis strategy are introduced. From which the local optimal diagnosis strategy is determined when the local optimal test points have been introduced by using the test information entropy, furthermore, the global optimal diagnosis strategy is determined by coalescing the local optimal diagnosis strategies. At last, the validity of the method is illustrated by an example from which the conclusion can be drawn that it is an optimal diagnosis strategy and the complexity of computation can be reduced.

A test points selection method for analog fault dictionary techniques

The problem of test point selection is important in the area of fault diagnosis and circuit testing. In this paper, the problem of near optimum test points set selection for analog fault dictionary is considered. This problem is formulated as a depth-first graph search problem. So the test points selection progress is transformed to a graph node expanding progress. The graph node construction method and node expanding procedure are given also. The proposed graph search method guarantee a maximum information increase of S opt by adding a test point to S opt each time, where S opt is the desired test points set used on the path from current node to root node. So a global minimum test points set can be more likely achieved than other methods. The results of statistical experiments indicate that the proposed method more accurately finds the global minimum set of test points without increasing time complexity; therefore, it is a good solution to minimize the size of test points set.

Application of Heuristic Graph Search to Test-Point Selection for Analog Fault Dictionary Techniques

The near-optimal test-point set selection for an analog fault dictionary is formulated as a heuristic depth-first graph search problem. Then, the test point selection process becomes a graph-node-expanding process. During the process of graph expansion, the information-theoretic concepts of entropy are used to develop the criterion for how to choose an intermediate node to expand. If a graph node has already isolated those faults that are hard isolate, then the residual faults can easily be isolated. The difficulty of isolating a given fault is evaluated by the information-theoretic concept of entropy. The harder that a fault is isolated, the larger the entropy value it will have. Statistical experimental results indicate that the proposed method more accurately finds the global minimum set of test points than other methods; therefore, it is a good solution to minimize the size of the test point set.

Test Points Selection for Analog Fault Dictionary Techniques

The test points selection problem for analog fault dictionary is researched extensively in many literatures. Various test points selection strategies and criteria for Integer-Coded fault wise table are described and compared in this paper. Firstly, the construction method of Integer-Coded fault wise table for analog fault dictionary is described. Secondly, theory and algorithms associated with these strategies and criteria are reviewed. Thirdly, the time complexity and solution accuracy of existing algorithms are analyzed and compared. Then, a more accurate test points selection strategy is proposed based on the existing strategies. Finally, statistical experiments are carried out and the accuracy and efficiency of different strategies and criteria are compared in a set of comparative tables and figures. Theoretical analysis and statistical experimental results given in this paper can provide an instruction for coding an efficient and accurate test points selection algorithm easily.

Comparison of NIST and Wavelet Transform Test Point Selection Methods For a Programmable Gain Amplifier

A comparison of test point selection methods using a linear matrix model based on the National Institute of Standard method and wavelet transform to reduce the number of test points, and hence, the test time, for a programmable gain amplifier (PGA) was investigated. The concept of integral nonlinearity (INL) was used to predict the performance of the PGA. It was found that the NIST method retained the original properties of the INL of the PGA. As such, the NIST method is able to predict the test responses of the PGA much more accurately than the wavelet transform method, using only 3 test points instead of the 155 test points as required in a full test.

Hard-Fault Detection and Diagnosis During the Application of Model-Based Data Converter Testing

The concept of model-based test was developed in order to reduce the production test effort for data converters (Cherubal and Chatterjee (IEEE Trans Circuits Syst part I 50(3):317---327, 2003); Stenbakken and Souders (1985) Modelling and test point selection for data converter testing. In: ITC, Int Test Conf, pp 813---817; Wegener and Kennedy (IEEE Trans Circuits Syst I 51(1):213---217, 2004); Wrixon and Kennedy (IEEE Trans Instrum Meas IM-48(5):978---985, 1999)). In applying this concept, a vector of model parameters is determined for each device under test (DUT). Typically, this model parameter vector is merely used to calculate the DUT performance characteristic which is then subject to specification-oriented testing. However, each element of the model parameter vector represents an independent error source which contributes to performance degradations; thus, the model parameter vector can be viewed as a signature of the error sources. In this work, analyzing the error source signature is used to devise a model-based methodology for hard-fault detection and diagnosis. We investigate conditions under which hard-faults are detectable/diagnosable in spite of masking effects due to manufacturing process variations. In particular, we show that taking the model parameter vector as the fault signature is optimal as it minimizes the masking effects and thus maximizes detectability/diagnosibility.

Field Test Results of the E-VSB System in Korea

This paper presents and analyses field test results of the Enhanced-VSB (E-VSB) system proposed by Zenith/ATI and adopted as an Advanced Television Standard Committee (ATSC) standard. The possibility of successful reception at outdoor and indoor test points according to types of antennas, noise margin, and ease of reception are measured to analyse performance improvement of the E-VSB system. Verification of backward compatibility between the E-VSB and conventional 8-VSB systems, selection of test points, and configurations of the transmitter and the test vehicle are also presented. The field test results show that the E-VSB system does not only outperform the 8-VSB system at severe multipath environment such as indoor reception, but also is fully backward compatible with it

Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection

A new approach to an optimal analog test points selection is proposed. The described method uses ambiguity set concept and evolutionary computations to determine the optimal set of analog test points. After a brief introduction to analog testing and genetic algorithms, the proposed strategy is explained. The presented evolutionary approach is illustrated by a practical example of analog circuit and by a series of hypothetical circuits. The efficiency of the technique is compared with a method based on entropy index, and the obtained results are discussed

Process Analysis of Foam Injection Moulding with Physical Blowing Agents

Through the application of structural foam injection moulding it is possible to reduce cycle time and thereby to attain economic benefits compared with conventional injection moulding. The highest savings potential is offered by the cooling phase. To determine possible savings, the surface temperature distribution of the parts produced was measured by infrared thermography after identical cooling times of compact and foamed parts. The cooling time was then shortened at selected test points to the minimum necessary for stable demoulding. A direct comparison between compact and foamed parts shows that, for a filled polypropylene, a cooling time reduction of up to 25 % is possible with a wall thickness of 4 mm. An influence of the process parameters and geometric boundary conditions was also established. The causes of these phenomena are analysed and discussed in detail.