RISE OF AI-POWERED ROOT CAUSE ANALYSIS: REVOLUTIONIZING PROBLEM SOLVING IN MODERN SYSTEMS
Keywords:
AI-powered Root Cause Analysis (RCA), Machine Learning, Anomaly Detection, Natural Language Processing (NLP), Predictive MaintenanceAbstract
This comprehensive article explores the transformative impact of AI-powered root cause analysis (RCA) across various industries. It delves into the core components and key technologies driving AI-powered RCA, including machine learning, anomaly detection, and natural language processing. The article compares traditional RCA methods with AI-driven approaches, highlighting AI's significant advantages in speed, accuracy, and scalability. It examines specific IT operations and manufacturing applications, demonstrating how AI-powered RCA is revolutionizing problem-solving and optimization in these sectors. The benefits of implementing AI-powered RCA are discussed in detail, emphasizing improved operational efficiency, cost reduction, and enhanced system reliability.
References
IBM, "IBM X-Force Threat Intelligence Index 2021," 2021. [Online]. Available: https://www.ibm.com/security/data-breach/threat-intelligence
Gartner, "Gartner Survey Reveals 66% of Organizations Increased or Did Not Change AI Investments Since the Onset of COVID-19," 2020.
[Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2020-10-01-gartner-survey-revels-66-percent-of-organizations-increased-or-did-not-change-ai-investments-since-the-onset-of-covid-19
McKinsey & Company, "The state of AI in 2021," 2021. [Online]. Available: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2021
Y. Wang, C. Wu, Z. Ji, B. Wang, and Y. Liang, "Non-Parametric Change-Point Detection for Multivariate Time Series Based on Deep Learning," IEEE Transactions on Reliability, vol. 69, no. 1, pp. 154-164, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8718305
L. Jiang, D. Wen, and Y. Yang, "A Deep Autoencoder-Based Approach for Anomaly Detection in Industrial Control Systems," IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8393-8402, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9376631
M. Nakata, Y. Watanabe, K. Fujiwara, and Y. Suwa, "Improving Incident Management Process by Natural Language Processing Techniques," IEEE Access, vol. 8, pp. 163984-163999, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9187600
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525-41550, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8681044
J. Lee, H. Davari, J. Singh, and V. Pandhare, "Industrial Artificial Intelligence for industry 4.0-based manufacturing systems," Manufacturing Letters, vol. 18, pp. 20-23, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2213846318300300
Z. Yang, J. Wang, and T. Chen, "An Ensemble Approach to Root Cause Analysis in Complex Manufacturing Systems," IEEE Transactions on Reliability, vol. 68, no. 4, pp. 1263-1279, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8704943
J. Penrose, "ARC Advisory Group: Proactive Asset Management with IIoT and Analytics," ARC Advisory Group, 2017. [Online]. Available: https://www.arcweb.com/blog/proactive-asset-management-iiot-analytics