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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 09 | Sep 2026

p-ISSN: 2395-0072

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SMART MACHINE LEARNING APPROACHES FOR DIAGNOSIS OF ELECTRONIC CIRCUITS FAULTS. Tahir Bin Bashir1 1 Masters in Electronics and Communication Engineering, Punjab Technical University

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Abstract - With the rapid expansion of applications in

interruptions, safety concerns, equipment damage, or financial losses. Therefore, the timely and accurate identify cation of circuit faults is essential for maintaining system performance and reliability.

commercial aviation, automotive engineering, healthcare, and consumer electronics, the demand for intelligent and adaptive surveillance systems is increasing. One of the important challenges in these applications is the timely identification and diagnosis of faults in electronic circuits. Conventional faultdetection techniques can be useful in controlled environments; however, their effectiveness may be limited when dealing with complex systems that require greater scalability, flexibility, and real-time operation. Recent developments in artificial intelligence (AI) and machine learning (ML) have created new possibilities for developing more accurate and adaptable approaches to fault detection and diagnosis. In this study, different learning-based techniques, including deep learning and supervised and unsupervised machine-learning approaches, are considered for identifying circuit-level deviations in both analog and digital systems. The performance of these approaches will be examined in terms of their detection accuracy, error rates, computational requirements, and overall effectiveness. The evaluation will involve simulation platforms, benchmark datasets, and, where available, real-world data to assess the practical suitability of the proposed approaches. The application of intelligent faultdetection systems can also contribute to improved risk assessment and risk-management practices by enabling faults to be identified at an early stage. Future research may further examine the usability, user satisfaction, adaptability, and flexibility of such systems under different operating conditions.

Traditional approaches to fault diagnosis often depend on predefined rules, expert knowledge, or manual inspection. Although these methods can be effective for relatively simple systems, they may become less practical when circuits contain complex structures and large volumes of operational data. Machine learning (ML) provides an alternative approach by enabling computational models to identify patterns and unusual behaviour from available data. Such techniques can assist in detecting deviations from normal operating conditions and may improve the efficiency of faultdiagnosis processes. This review therefore examines the role of machine learning in electronic fault detection and diagnosis, with particular attention to its potential for supporting intelligent, flexible, and adaptable electronic systems. It also considers how datadriven approaches may contribute to improving the reliability and practical performance of modern electronic technologies.

2. BACKGROUND AND MOTIVATION The rapid growth of technology and the increasing dependence of industries on electronic and digital systems have created a strong need for reliable and intelligent faultdetection methods. Modern electronic systems are becoming more complex, while their designs and operating characteristics may vary considerably across manufacturers and applications. As a result, conventional diagnostic approaches based mainly on fixed rules, predefined conditions, or manual inspection may face difficulties when they are applied to large, complex, and continuously changing systems.

Key Words: Artificial Intelligence (AI), Machine Learning (ML), Fault Detection, Fault Diagnosis, Electronic Circuits, Analog Circuits, Digital Circuits, Deep Learning, Supervised Learning, Unsupervised Learning, Circuit-Level Faults, Real-Time Fault Detection, Intelligent Surveillance Systems, Fault Classification, Detection Accuracy, Error Rate, Computational Efficiency, Benchmark Datasets, Simulation, Risk Assessment, Risk Management, Adaptive Systems.

Machine learning (ML) has emerged as a promising approach for addressing these challenges. Its applications are no longer limited to research environments and specialized industries; ML-based technologies are now widely used in areas such as image processing, speech recognition, healthcare, and automated decision-making. A major advantage of ML is its ability to identify patterns within large datasets and detect deviations from normal system behaviour. This capability makes it particularly useful for the identification and diagnosis of faults in electronic systems.

1. INTRODUCTION Electronic circuits and systems form an essential part of modern technological infrastructure and support a wide range of applications, including telecommunications, aviation, medical equipment, industrial automation, and data-processing systems. As these technologies become increasingly complex and widely used, ensuring their reliable operation has become an important concern. Even a minor fault in an electronic system can lead to operational

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 09 | Sep 2026

p-ISSN: 2395-0072

www.irjet.net

4. TRADITIONAL FAULT DIAGNOSIS TECHNIQUES.

In industrial environments, effective fault detection can help extend the useful life of equipment, reduce unnecessary maintenance, and minimize operational costs. Early identification of abnormal conditions can also prevent minor faults from developing into more serious and expensive failures. In this way, ML-based diagnostic systems can contribute to improved efficiency, reliability, and flexibility in industrial operations.

Many clinical methods are used in errors detection and correction in the electronic circuit in ancient times. Each contributor has a marked influence on systematic defective diagnosis. Performance limitations, though, are not suitable for complex and large systems. Methods that are important include: Testing manually with an oscilloscope involves highly reliant on humans and careful observation of waves. The method can be useful in the lesser scenario. But it takes a considerable amount of time to achieve a result. Further, it is not practical for complex systems. ATE is widely used for onboard circuit diagnostics because of its accuracy and precision. Yet, often the use is dictated by increased cost, limited flexibility and unique configuration. Voltage and power signatures are examined and their limitations tested for anomaly detection. It is important to capture the needed behaviors; however, they are usually quite inflexible to different errors. Why? Specialist systems that function successfully in landscapes are built upon predetermined rules using domain-specific knowledge. Due to their rigid structure, they cannot be made to conform, making them balk on a larger scale when it comes to making mistakes or. Standard methods for diagnosing problems are not adequate because of lack of scalability, limited adaptability and insufficient automation efficiencies of modern electronic systems.

The aerospace sector is one area where these developments are particularly important because system reliability, safety, and performance are closely associated with the timely detection of faults. Intelligent diagnostic techniques have the potential to improve fault-detection accuracy and coverage while supporting faster responses to abnormal operating conditions. They may also reduce dependence on extensive manual inspection and improve the overall efficiency of maintenance activities. Therefore, the integration of machine learning with electronic fault-diagnosis systems represents an important step toward developing intelligent industrial technologies that can achieve a better balance between operational reliability, safety, and cost-effectiveness.

3. FAULT TYPES IN ELECTRONIC CIRCUITS. Different errors compromise the safety of electronic cycles. It is often challenging to identify errors that are unique using conventional clinical methods. Common categories include. Open circuit faults: Loss of signal transmission due to disconnected or broken circuit paths. Short circuit faults: Unexpected current paths that can cause excessive power waste and possible damage. Drift faults: The gradual decline in components over time, such as a stop tolerance or aging condensator changes, which change the expected circuit behavior. Intermittent faults: Errors that occur by occasional and can be self -sufficient, they become particularly difficult to isolate or repeat them during testing. Stuck-at faults: In a digital circuit, examples interfere with a signal permanently fixed at logic level '0' or '1,' the general circuit function. Noisy-inspired errors: disturbances triggered by external or environmental interventions, which distort signal conditions and affect performance. These errors not only represent real challenges for circuit reliability, but also act as the basis for creating a dataset used in Benchmark models. By systematic catalogs and mimicking the situation for such a mistake, researchers can create representative data sets that enable the machine learning algorithm to better understand, classify and predict defects, eventually improved clinical accuracy and efficiency in both analog and digital systems.

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Volume: 13 Issue: 09 | Sep 2026

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5. MISDIAGONISIS, MACHINE LEARNING TECHNIQUE.

6. COMPARATIVE ANALYSIS OF ML TECHNIQUES When comparing different machine learning methods, we need to be cautious of the varying accuracy, calculation complexity, type of datasets, and flaw handling capabilities. The binary error support by SVM will enhance class discussion and make classroom learning more effective. Despite this, the Random Forest (RF) is flexible and can work well on noisy data. Deep learning models are very powerful models, but they need large label data and big calculation power. Clothing models or "hybrid techniques" that combine the clarity of traditional machine learning with the flexibility of deep learning are often the best approach.

Nowadays complex problems need intelligent and timely answers in machine learning. Most monitors use the algorithm of SVM, RF, KNN, or Ann, which is in tune with what they need to do and so they use them the most. RF is proven to be effective in noisy and high-dimensional data, SVMs then provides an efficient method for sorting through error classes while KNN helps you to figure out classes that would be hard to separate. because of the lack of control datasets, Kerosse preferrable to unsuversed learning. But these intelligent learning methods can produce hidden patterns and behavioral innovation because there is no predefined way to correct errors with them. Covic Neural Networks (CNN) and other architectures are useful for spotting protyrely patterns in circuit signals. It greatly helps in today’s high technology. Short term memory models and more complex nervous system models are good at recording what you’re going through at the time. This will be the most accurate, it is most accurate with this gerc filesystems. This will have some parts that need to be accurate and that is always better than having not full or half no real information at all. Using new computer architectures is necessary to analyze circuit signals, if you want sped up results. This new method uses diagrams. RNNs and LSTMs are very up to the point for a temporary usage of sequential data.

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7. TOOLS AND DATASETS USED IN LITERATURE Popular software used for simulation-based datasets include LTSPICE, Multisim, and MATLAB/Simulink. The algorithms are extensively used using Python Library such as ScikitLarn, TensorFlow, Keras. Easy-to-use programs named Weka and Orange have been introduced to the machine learning developers’ platform using graphical user interface. The benchmark datasets are NASA Prognostics dataset modified to the dataset from circuit simulator, as well as the practical datasets with measurement components. One of the most important challenges that this region faces is a lack of available and consistent data sets, particularly electronic circuit defect data sets.

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 09 | Sep 2026

p-ISSN: 2395-0072

www.irjet.net

learning. Routes Demonstrate Ongoing Reliability and Safety Improvements from Machine Learning.

8. CHALLENGES AND RESEARCH GAPS All progress is good, yet to solve these problems. - Some Circuit Failure Detection Has No Label Data. It won’t be easy to diagnose lots of errors all at once. To solve these problems, we need to get more people to cooperate in dataset improvement, model efficiency, and distinctive AI inclusion.

10. CONCLUSIONS Machine learning delivered the defects prediction of electronic cycles, a highly accurate and scalable diagnosis tool. Although the standards remain the same, ML seems better equipped to handle modern cycles than traditional methods. He pointed out that although these methods may be useful in “real life”, there is still work to do, in regard to both data set accessibility, model strength, interpretation, and calculation efficiency. Review focuses on hybrid and advanced ML techniques to devise intelligent defects.

9. APPLICATIONS AND INDUSTRIAL RELEVANCE In various domains, machine learning-based defect diagnosis generates waves.?... These are some of the most essential apps: Maintenance in the future can reduce operating time in production.

REFERENCES

Car electronics rely on it to monitor their ECU. Aviation systems and navigation systems are closely monitored by this important aspect. Consumer electronics can benefit from real-time self-diagnosis.

1) A. Roy, S. Chatterjee, and P. K. Goswami, “Fault detection in analog circuits using SVM,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 2, pp. 398–408, 2013.

It is active detection of deviations in the power system, as in Smart Nett and IoT. The illustrations indicate both economic benefits and security improvements that offer the possibility of incorporating machine learning into misdiagnosis.

2) X. Zhang, Y. Wang, and L. Liu, “Machine learning-based fault diagnosis: A review,” IEEE Access, vol. 9, pp. 41290–41307, 2021. 3) S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson, 2009. 4) [Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. 5) NASA Prognostics Data Repository, NASA Ames Research Center - prognostics datasets (bearing, electronics, etc.). 6) J. Xiong, L. Yu, and Z. Li, “Fault diagnosis for analog circuits by using EEMD, relative entropy and SVM,” Sensors (Basel), 2016. 7) Y. Lei, J. Lin, Z. He, and Y. Zi, “Applications of machine learning to intelligent fault diagnosis - a survey,” Mechanical Systems and Signal Processing / related reviews, 2020. 8) S. Qiu, H. Wang, and R. He, “Deep learning techniques in intelligent fault diagnosis: a survey and outlook,” Sensors (Basel) / MDPI, 2023. 9) N. Nedjah and L. A. de Carvalho, “Fault diagnosis in analog circuits using swarm intelligence: methods and applications,” Scientific Reports / MDPI / PMC article (review), 2023.

3. FUTURE DIRECTIONS

10) L. Ji, C. Fu, and W. Sun, “Soft fault diagnosis of analog circuits using convolutional neural networks,” Applied Intelligence, 2021.

Aspects of research looking forward to finding out. Reinforcement learning for misconnection that is smart and flexible. - Fevered Learning is a method that helps in distributed diagnosis while keeping sensitive data safe. - Use of transmission learning to use the models in different circuit families. Create machine learning solutions for AMBED systems using edge and FPGA technologies. - We focus on Clear AI to foster transparency and ensure trust in industrial applications. - Deep brain learning is a promising long-term solution for complex error analysis, explains quantum

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11) J. Yang, T. Gao, and S. Jiang, “Dual-input fault diagnosis model based on SE-MSCNN for analog circuits,” Applied Intelligence, vol. 53, pp. 7154–7168, 2023. 12) R. Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, 2006. 13) S. Mohanty and E. Kougianos, “Fault detection in VLSI circuits using machine learning techniques,” IEEE Computer, vol. 52, no. 5, pp. 30–38, 2019.

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 09 | Sep 2026

p-ISSN: 2395-0072

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14) M. A. Ochoa, P. M. Ramos, and A. Cruz, “Intelligent fault diagnosis in mixed-signal circuits using random forests,” Microelectronics Reliability, vol. 114, pp. 113–125, 2020.

BIOGRAPHIES

15) C. Zhang, D. Zha, L. Wang, and N. Mu, “A novel analog circuit soft-fault diagnosis method based on convolutional neural network and backward difference,” Symmetry, 2021.

1Tahir

Bin Bashir, Masters in Electronics and Communication Engineering, Punjab Technical University)

16) R. Yan, R. Gao, and X. Chen, “Wavelets and their applications for fault diagnosis - review,” Mechanical Systems and Signal Processing, 2012. 17) H. Wang, C. Hu, and J. Zhou, “Deep learning for real-time fault diagnosis in IoT-enabled electronic systems,” IEEE Internet of Things Journal, 2021. 18) Z. Chen, K. He, and J. Wei, “Transfer learning-based fault diagnosis for electronic circuits,” IEEE Transactions on Instrumentation and Measurement, 2022. 19) [19] F. Ahmad, S. Khan, and A. Hussain, “Explainable AI for fault diagnosis in industrial electronics,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5162–5171, 2022. 20) G. Sampedro, J. M. P. de la Ossa, and R. P. de la Rosa, “Exploring machine learning-based fault monitoring for additive manufacturing: A survey and experimental review,” Sensors, MDPI, 2022. 21) S. Q. Xie and L. Zhang, “LSTM-based sequence models for transient-fault detection in circuits,” International Journal of Electronics and Communications, 2020. 22) A. K. Singh and P. K. Gupta, “Edge and FPGA-based deployment of ML models for real-time fault diagnosis,” IEEE Embedded Systems Letters, 2021. 23) H. Pan, X. Li, and Z. Sun, “Transferable fault diagnosis of electronic circuits via domain adaptation,” IEEE Transactions on Industrial Electronics, 2020. 24) K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. CVPR, 2016 (useful for CNN backbone designs applied to spectrogram/image representations of signals). 25) D. M. Hawkins and S. J. B. T. E. Box, “Statistical process monitoring and signature-analysis techniques for circuit testing,” Testing and Quality Assurance Journals, classic references (useful background for signature-based methods and limit-scanning approaches). 26) Case Western Reserve University (CWRU) Bearing Data Center - public dataset often used for fault-diagnosis algorithm benchmarking (useful analog dataset for method development). 27) Recent tutorials and overviews on hybrid ML models for fault diagnosis: collections in IEEE Access, Sensors (MDPI) and Mechanical Systems and Signal Processing (2020–2024) - useful for cross-referencing recent hybrid-model studies..

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