Intelligent Fault Detection and Identification System for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works

Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems

Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

The aim of this research is to enhance Oil and Gas (O&amp;G) construction risk assessment using Fuzzy-based Failure Model Effect Analysis (FMEA) through the lens of O&amp;G project managers in the U.S. A mixed-method approach was adopted for data collection, analysis, and processing, including semi-structured interviews with project managers to identify the key risks facing O&amp;G construction projects; a Fuzzy-based FMEA to quantitatively analyse the level of significance of O&amp;G risks; surveys to rank the assessment dimensions of the developed model and their components; and open-ended surveys to validate and verify the assessment model and its outputs, further expanding on the root causes of significant risks based on the assessment outputs, and to propose mitigation strategies for these risks. The research identified 41 risk factors classified under six categories, namely: management, technical and quality, financial and economic, health, safety, environmental, legal, and stakeholders' risks. In addition, the risk assessment revealed that non-compliance with PPE regulations emerged as the most significant risk factor across all categories of O&amp;G risks. This study offers valuable insights by assisting practitioners in better understanding the significant O&amp;G risks that need to be addressed to ensure the successful execution and completion of O&amp;G projects.</p

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

The aim of this research is to enhance Oil and Gas (O&amp;G) construction risk assessment using Fuzzy-based Failure Model Effect Analysis (FMEA) through the lens of O&amp;G project managers in the U.S. A mixed-method approach was adopted for data collection, analysis, and processing, including semi-structured interviews with project managers to identify the key risks facing O&amp;G construction projects; a Fuzzy-based FMEA to quantitatively analyse the level of significance of O&amp;G risks; surveys to rank the assessment dimensions of the developed model and their components; and open-ended surveys to validate and verify the assessment model and its outputs, further expanding on the root causes of significant risks based on the assessment outputs, and to propose mitigation strategies for these risks. The research identified 41 risk factors classified under six categories, namely: management, technical and quality, financial and economic, health, safety, environmental, legal, and stakeholders' risks. In addition, the risk assessment revealed that non-compliance with PPE regulations emerged as the most significant risk factor across all categories of O&amp;G risks. This study offers valuable insights by assisting practitioners in better understanding the significant O&amp;G risks that need to be addressed to ensure the successful execution and completion of O&amp;G projects.</p

Expert Evaluation of ChatGPT Performance for Risk Management Process based on ISO 31000 Standard

ChatGPT is widely known for its ability to facilitate knowledge exchange, support research endeavours, and enhance problem-solving across various scientific disciplines. However, to date, no empirical research has been undertaken to evaluate ChatGPT's performance against established standards or professional guidelines. Consequently, the present study aims to evaluate the performance of ChatGPT for the risk management (RM) process based on ISO 31000 standard using expert evaluation. The authors (1) identified the key indicators for measuring the performance of ChatGPT in managing construction risks based on ISO 31000 and determined the key assessment criteria for evaluating the identified indicators using a focus group session with Iraqi experts; and (2) quantitatively analysed the level of performance of ChatGPT under a fuzzy environment. The findings indicated that ChatGPT's overall performance was high. Specifically, its ability to provide relevant risk mitigation strategies was identified as its strongest aspect. However, the research also revealed that ChatGPT's consistency in risk assessment and prioritization was the least effective aspect. This research serves as a foundation for future studies and developments in the field of AI-driven risk management, advancing our theoretical understanding of the application of AI models like ChatGPT in real-world risk scenarios

Robust Adaptive Control of Knee Exoskeleton-Assistant System Based on Nonlinear Disturbance Observer

This study presents a control design of an angular position for the exoskeleton knee assistance system based on a model reference adaptive control (MRAC) strategy. Three schemes of the MRAC design have been proposed: the classical MRAC, MRAC with an adaptive disturbance observer, and MRAC with a nonlinear observer. The stability analysis for each scheme has been conducted and developed based on the Lyapunov theorem to prove the uniform ultimate bound of tracking and estimation errors. In addition, the adaptive laws have been developed for the proposed schemes according to the stability analysis. The effectiveness of the proposed state and output feedback controllers has been verified via computer simulation. The results based on numerical simulation have shown that the MRAC with a nonlinear observer could give better robustness characteristics and better performance in terms of tracking and estimation errors as compared to the other controllers