KKCV-GA-Based Method for Optimal Analog Test Point Selection

Hard (open and short) faults and discrete parameter faults (DPF) are the mostly used fault models in simulation-before-test (SBT) method. Because that the parameter of analog element is continuous, the DPF can not elaborately characterize all possible continuous parameter faults (CPF) occurring in analog circuit, let alone the double soft fault. To address such problem, a genetic algorithm (GA) based simulation after test (SAT) fault diagnosis method is proposed in this paper. The fault diagnosis is transformed into an optimization problem. The genes represent the parameter values of potential faulty components. Our target is to minimize the difference between the actual faulty response and the GA simulated response. The chromosome that minimize the difference gives the solution. This method does not save all possible faults in advance whereas it can diagnosis single and double continuous faults. The effectiveness of the proposed method is examined by using filter circuit examples.

Section: A Illustrative Examplementioning

confidence: 99%

Section: A Illustrative Examplementioning

confidence: 99%

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Multiple Soft Fault Diagnosis of Analog Filter Circuit Based on Genetic Algorithm

Yang

2020

“…Hence, these four faults are distinguishable and the two fault source and can be correctly located under the assumption that only hard fault occurs to them. The hard fault is simple and still the mostly used fault model [2,3]. Because that the parameter value of analog element is continuous real number, the faulty parameter p could be any positive real number, p∈ ℝ .…”

Section: A Fault Locationmentioning

confidence: 99%

Genetic Algorithm Based Faulty Parameter Identification for Linear Analog Circuit

Yang

2020

Faulty parameter identification is vital to prediction of remaining useful life of circuit under test (CUT). Based on the transfer function of the CUT, we use measured faulty response to reversely deduce the possible faulty parameters. It is known that the response is the function of the analog parameters. Usually, the number of independent response is much fewer than that of the analog component. The underdetermined equations have infinite solutions. In other words, there are lots of combinations of analog parameters that can generate the same faulty response. Hence, it is more valuable to deduce the possible fault parameter range within which all faults can generate the same measured response. The deduction is accomplished by using genetic algorithm. The length of chromosome equals to the number of analog component. Each gene represents a component's parameter value. Based on the gene values and the transfer function, each individual has a simulated response. Our target is to find all possible individuals viz., parameter combinations, which minimize the difference between the simulated and measured faulty response. In the final solutions, it is easy to find the minimal and maximum fault parameters, viz., extreme points. The possible faulty parameter rage is determined by these two extreme points because that the transfer function and parameters of analog element are continuous. The effectiveness of the proposed method is examined by using filter circuit examples.

“…In terms of test optimization of the binary system, scholars have made extensive research and put forward many optimization methods, mainly including sorting methods based on information theory [21][22][23] and search algorithm based on combination optimization [24][25][26][27]. The former mainly uses information entropy to define the test importance of fault detection and fault isolation and takes it as the weight of test, preferentially selects the test with high weight until it meets the requirements of testability index.…”

Section: Introductionmentioning

confidence: 99%

Testability Modeling and Test Point Optimization Method of Multi-State System

Wang

2020

Existing testability models are difficult to describe the multi-state characteristics of the system, so it is necessary to study the testability modeling method applicable to multi-state systems. A testability model with structure and function as the object is established in this paper. In order to describe the relationship between system state and test, the calculation method of the detectable state set of the test set is introduced. In order to quantitatively describe the testability of the system state, the concept of state detection rate is proposed for the first time. A test point optimization method that comprehensively considers the system fault detection rate, fault isolation rate, and state detection rate under the constraints of test cost is proposed. A numerical example shows that the best test set obtained by this method cannot only complete the system fault detection and isolation, but also obtain more system state.