Multiple parametric fault diagnosis using computational intelligence techniques in linear filter circuit

Kalpana, V.; Maheswar, R.; Nandakumar, E.

doi:10.1007/s12652-020-01908-0

Cited by 10 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2020, Kalpana et al [26] presented an Extreme Learning Machine-based Autoencoder for parametric fault diagnosis (ELM-AE-FD) in the analog circuit. Additionally, Single and multiple parametric fault analyses were considered for simulation before the test model.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep Q-Learning with Bit-Swapping-Based Linear Feedback Shift Register fostered Built-In Self-Test and Built-In Self-Repair for SRAM

Ahmed

Alnatheer

2022

Micromachines

View full text Add to dashboard Cite

Including redundancy is popular and widely used in a fault-tolerant method for memories. Effective fault-tolerant methods are a demand of today’s large-size memories. Recently, system-on-chips (SOCs) have been developed in nanotechnology, with most of the chip area occupied by memories. Generally, memories in SOCs contain various sizes with poor accessibility. Thus, it is not easy to repair these memories with the conventional external equipment test method. For this reason, memory designers commonly use the redundancy method for replacing rows–columns with spare ones mainly to improve the yield of the memories. In this manuscript, the Deep Q-learning (DQL) with Bit-Swapping-based linear feedback shift register (BSLFSR) for Fault Detection (DQL-BSLFSR-FD) is proposed for Static Random Access Memory (SRAM). The proposed Deep Q-learning-based memory built-in self-test (MBIST) is used to check the memory array unit for faults. The faults are inserted into the memory using the Deep Q-learning fault injection process. The test patterns and faults injection are controlled during testing using different test cases. Subsequently, fault memory is repaired after inserting faults in the memory cell using the Bit-Swapping-based linear feedback shift register (BSLFSR) based Built-in Self-Repair (BISR) model. The BSLFSR model performs redundancy analysis that detects faulty cells, utilizing spare rows and columns instead of defective cells. The design and implementation of the proposed BIST and Built-in Self-Repair methods are developed on FPGA, and Verilog’s simulation is conducted. Therefore, the proposed DQL-BSLFSR-FD model simulation has attained 23.5%, 29.5% lower maximum operating frequency (minimum clock period), and 34.9%, 26.7% lower total power consumption than the existing approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The simulation outcomes of the DQL-BSLFSR-FD method are compared with existing methods, such as Extreme Learning Machine-based Autoencoder for fault diagnosis (ELM-AE-FD) [26], and built-in self-test (BIST) model-based post package inspections (PPI) for fault diagnosis (BIST-PPI-FD) [27], respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Q-Learning with Bit-Swapping-Based Linear Feedback Shift Register fostered Built-In Self-Test and Built-In Self-Repair for SRAM

Ahmed

Alnatheer

2022

Micromachines

View full text Add to dashboard Cite

show abstract

“…In order to solve the above problems, data-driven artificial intelligence methods such as Support Vector Machine (SVM) [10] and Extreme Learning Machine (ELM) [11] are widely entered into the research field, providing feasible technical support for analog circuit fault diagnosis. Literature [12] proposed a conventional time-domain feature vector based on the impulse response characteristics of a control system, and according to the Least Squares Support Vector Machine (LSSVM) experiments show that the classification performance of LSSVM can be improved by using the improved vector.…”

Section: Introductionmentioning

confidence: 99%

A Complex Analog Circuit Fault Diagnosis Method based on IMODA Improved Deep Belief Network

Gong,

2024

AJST

View full text Add to dashboard Cite

Aiming at the problems of time-consuming and low diagnosis accuracy during the unsupervised training process of traditional DBN, an analog circuit fault diagnosis method based on Improved Multi-Objective Dragonfly Optimized Deep Belief Network (IMODA-ADBN) is proposed. The method employs an improved MODA algorithm instead of the BP algorithm, which improves the classification accuracy of the network and ameliorates the problem of being prone to falling into local optima. The algorithm is tested on three multi-objective mathematical benchmark problems and compared with three well-known meta-heuristic optimization algorithms such as MODA, MOPSO and NSGA-II, and the results demonstrate the stability of the IMODA-ADBN network model. Finally, IMODA-ADBN is applied to the diagnostic experiments of a two-stage quad op-amp dual second-order low-pass filter, and the results show that the method improves the classification accuracy and diagnostic rate while guaranteeing the convergence speed, and is able to effectively realize the classification and localization of difficult faults.

show abstract

“…To solve the above problems, data-driven artificial intelligence methods such as support vector machine (SVM) [14] and extreme learning machine (ELM) [15] have widely entered the research field, providing feasible technical support for analog circuit fault diagnosis. Long et al [16] proposed a method based on a conventional time-domain feature vector based on the impulse response characteristics of the control system, and according to the last squares SVM (LSSVM) experiments showed that the classification performance of LSSVM can be improved using the improved vector.…”

Section: Introductionmentioning

confidence: 99%

“…Long et al [17] used Mahalanobis distance (MD) particle swarm optimization (PSO) to optimize the classifier and reasonably select the feature vectors so as to improve the classification accuracy. Chen et al [18] proposed a double-chain quantum genetic algorithm (DCQGA) based on DCQGA and experimentally showed that the overall best fit and classification accuracy of DCQGA-SVM were improved.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Analog Circuits Based on Information Fusion in the Time-Frequency Domain

Gong¹,

An²

2023

HSET

View full text Add to dashboard Cite

To address the low fault feature extraction capability in analog circuits for component classification in analog circuits. Convolutional Block Attention Module-multiple-convolutional neural networks (CBAM-MIL-CNN) is proposed. The model has a better comprehensive performance in fault diagnosis experiments for circuits with secondary four-operator dual second-order low-pass filters, and can effectively achieve efficient classification and localization of all faults.

show abstract

Multiple parametric fault diagnosis using computational intelligence techniques in linear filter circuit

Cited by 10 publications

References 32 publications

Deep Q-Learning with Bit-Swapping-Based Linear Feedback Shift Register fostered Built-In Self-Test and Built-In Self-Repair for SRAM

Deep Q-Learning with Bit-Swapping-Based Linear Feedback Shift Register fostered Built-In Self-Test and Built-In Self-Repair for SRAM

A Complex Analog Circuit Fault Diagnosis Method based on IMODA Improved Deep Belief Network

Fault Diagnosis of Analog Circuits Based on Information Fusion in the Time-Frequency Domain

Contact Info

Product

Resources

About