Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

Abstract

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.

Review of Launch Vehicle Engine PHM Technology and Analysis Methods Research

“…Non-statistical analysis methods such as Neural Networks (NN) and Fuzzy Logic (FL) are also included in quantitative knowledge fault detection. With the powerful ability in nonlinearity and adaptive learning capability, Neural Networks-based fault detection is widely used and has turned out to be one of the most mature non-statistical fault diagnosis tools [32,33]. The Neural Network-based fault prediction method is trained to learn based on the historical data provided, and then the constructed network structure is constructed to achieve the required accuracy for prediction, which is appropriate for the intelligent prediction of complex systems.…”

An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

“…The fault-free output voltage, . U o = U or + jU oj must satisfy (15). Therefore, all characteristic curves pass through the fault-free point (U or ,U oj ) [13].…”

“…The classifier-based fault diagnosis methods are generally implemented in three steps: collecting features of the circuit in different states, training the collected features with a classifier, and diagnosing the fault based on the training data. These methods typically adopt classifiers like support vector machine (SVM) [7], least squares support vector machine (LS-SVM) [8], extreme learning machine (ELM) [15,20], and so on. The SVM or LS-SVM algorithm is used to map the lower-dimensional nonlinear response space into the higher-dimensional feature space for effective classification.…”

Recent Trends in ELM and MLELM: A review

“…Active test generation algorithms cannot save time effectively and it lags in accuracy when the number of impulse-response samples decreases. Due to this computational complexity and classification theory of methods, ELM-based algorithm is used [28].…”