Engine Heartbeats and Predictive Diagnostics: Leveraging AI, ML, and IoT-Enabled Data Pipelines for Real-Time Engine Performance Optimization

Anil Lokesh Gadi

Authors

Anil Lokesh Gadi Manager, Cognizant Technology Solutions - US Corp, Plano, TX, USA. Author

Keywords:

Engine Performance Optimization, Predictive Maintenance, Prescriptive Maintenance, Industry 4.0, Internet of Things, Artificial Intelligence, Machine Learning, Digitalization, Data Pipelines, Engine Heartbeats, Condition Monitoring, Gas Turbine Engines, Real-Time Performance Assessment, Sensor Data Utilization, Operations Management System, Diagnostic Possibilities, Advanced Technologies, Production Equipment Data, Decision-Making Support, Industrial Revolution

Abstract

Engine performance optimization is a hot research area due to its significant impact on the cost of time-value products. The recent Fourth Industrial Revolution and the huge growth of data brought a revolution in the production sector. The day-to-day growing amount of data collected from production equipment leads to new diagnostic possibilities. Digitalization and the advent of advanced technologies such as Industry 4.0, Internet of Things systems, and Artificial Intelligence and Machine Learning algorithms have seen a significant shift from traditional condition-based maintenance to predictive and even prescriptive maintenance. Therefore, predictive diagnostics can recognize symptoms of malfunctions before they occur, while prescriptive diagnostics can predict the remaining service life of a processing unit. The result is the recommendation and implementation of the concept of engine heartbeats and data pipelines integrated with the operations management system. This system integrates a few high technological solutions and paradigms.

The focus is on the context of the industry and effectively utilizes as well as gives additional insights into improving engine condition monitoring based on the data. The focus is on engine heartbeats and presents an approach for getting detailed signals from the nacelle and further processing them to assess real-time engine performance. The presented studies conducted for a population of gas turbine engine units showed a high potential for the proposed approach. Innovative solutions that lead to reading the engine heartbeats are necessary for the future of the industry. To perform the mentioned approach, implemented data pipelines are recommended to be followed. They deliver efficient and decision-ready data to be used for real-time and offline decision-making support. Although sensor data is not a novelty, the effective use of it in the industry is a challenge nowadays because the variety is so vast that the number of solutions is endless.

References

Shashi Thota, Subrahmanyasarma Chitta, Vinay Kumar Reddy Vangoor, Chetan Sasidhar Ravi, & Venkata Sri Manoj Bonam. (2023). Few-Shot Learning in Computer Vision: Practical Applications and Techniques. Human-Computer Interaction Perspectives, 3(1), 29-58. https://tsbpublisher.org/hcip/article/view/83

Daruvuri, R., & Patibandla, K. (2023). Enhancing data security and privacy in edge computing: A comprehensive review of key technologies and future directions. International Journal of Research in Electronics and Computer Engineering, 11(1), 77-88.

Siddharth K, Gagan Kumar P, Chandrababu K, Janardhana Rao S, Sanjay Ramdas B, et al. (2023) A Comparative Analysis of Network Intrusion Detection Using Different Machine Learning Techniques. J Contemp Edu Theo Artific Intel: JCETAI-102.

Vankayalapati, R. K., Sondinti, L. R., Kalisetty, S., & Valiki, S. (2023). Unifying Edge and Cloud Computing: A Framework for Distributed AI and Real-Time Processing. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i9s(2).3348

Sikha, V. K., Siramgari, D., & Korada, L. (2023). Mastering Prompt Engineering: Optimizing Interaction with Generative AI Agents. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-E117. DOI: doi. org/10.47363/JEAST/2023 (5) E117 J Eng App Sci Technol, 5(6), 2-8.

Srinivas Rao Challa. (2023). The Role of Artificial Intelligence in Wealth Advisory: Enhancing Personalized Investment Strategies Through DataDriven Decision Making. International Journal of Finance (IJFIN), 36(6), 26–46.

Burugulla, J. K. R. (2022). The Role of Cloud Computing in Revolutionizing Business Banking Services: A Case Study on American Express’s Digital Financial Ecosystem. In Kurdish Studies. Green Publication. https://doi.org/10.53555/ks.v10i2.3720

R. Daruvuri, "Dynamic load balancing in AI-enabled cloud infrastructures using reinforcement learning and algorithmic optimization," World Journal of Advanced Research and Reviews, vol. 20, no. 1, pp. 1327-1335, 2023.

Pallav Kumar Kaulwar. (2023). Tax Optimization and Compliance in Global Business Operations: Analyzing the Challenges and Opportunities of International Taxation Policies and Transfer Pricing. International Journal of Finance (IJFIN) - ABDC Journal Quality List, 36(6), 150-181.

Kalisetty, S., Pandugula, C., & Mallesham, G. (2023). Leveraging Artificial Intelligence to Enhance Supply Chain Resilience: A Study of Predictive Analytics and Risk Mitigation Strategies. Journal of Artificial Intelligence and Big Data, 3(1), 29–45. Retrieved from https://www.scipublications.com/journal/index.php/jaibd/article/view/1202

Annapareddy, V. N., & Seenu, A. Generative AI in Predictive Maintenance and Performance Enhancement of Solar Battery Storage Systems.

Kannan, S. (2023). The Convergence of AI, Machine Learning, and Neural Networks in Precision Agriculture: Generative AI as a Catalyst for Future Food Systems. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3451

Daruvuri, R. (2023). Dynamic load balancing in AI-enabled cloud infrastructures using reinforcement learning and algorithmic optimization. World Journal of Advanced Research and Reviews, 20(1), 1327-1335.

Sondinti, L. R. K., Kalisetty, S., Polineni, T. N. S., & abhireddy, N. (2023). Towards Quantum-Enhanced Cloud Platforms: Bridging Classical and Quantum Computing for Future Workloads. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3347

Murali Malempati, Dr. P.R. Sudha Rani. (2023). Autonomous AI Ecosystems for Seamless Digital Transactions: Exploring Neural Network-Enhanced Predictive Payment Models. International Journal of Finance (IJFIN), 36(6), 47–69.

Karthik Chava, Dr. P.R. Sudha Rani, (2023) Generative Neural Models in Healthcare Sampling: Leveraging AI-ML Synergies for Precision-Driven Solutions in Logistics and Fulfillment. Frontiers in Health Informa (6933-6952)

Nagesh Boddapati, V. (2023). AI-Powered Insights: Leveraging Machine Learning And Big Data For Advanced Genomic Research In Healthcare. In Educational Administration: Theory and Practice (pp. 2849–2857). Green Publication. https://doi.org/10.53555/kuey.v29i4.7531

Polineni, T. N. S., abhireddy, N., & Yasmeen, Z. (2023). AI-Powered Predictive Systems for Managing Epidemic Spread in High-Density Populations. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3374

Challa, K. (2023). Transforming Travel Benefits through Generative AI: A Machine Learning Perspective on Enhancing Personalized Consumer Experiences. In Educational Administration: Theory and Practice. Green Publication. https://doi.org/10.53555/kuey.v29i4.9241

Nuka, S. T. (2023). Generative AI for Procedural Efficiency in Interventional Radiology and Vascular Access: Automating Diagnostics and Enhancing Treatment Planning. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3449

Chaitran Chakilam. (2022). Integrating Generative AI Models And Machine Learning Algorithms For Optimizing Clinical Trial Matching And Accessibility In Precision Medicine. Migration Letters, 19(S8), 1918–1933. Retrieved from https://migrationletters.com/index.php/ml/article/view/11631

Patra, G. K., Kuraku, C., Konkimalla, S., Boddapati, V. N., & Sarisa, M. (2023). Voice classification in AI: Harnessing machine learning for enhanced speech recognition. Global Research and Development Journals, 8(12), 19–26. https://doi.org/10.70179/grdjev09i110003

Venkata Bhardwaj Komaragiri. (2022). AI-Driven Maintenance Algorithms For Intelligent Network Systems: Leveraging Neural Networks To Predict And Optimize Performance In Dynamic Environments. Migration Letters, 19(S8), 1949–1964. Retrieved from https://migrationletters.com/index.php/ml/article/view/11633

Subhash Polineni, T. N., Pandugula, C., & Azith Teja Ganti, V. K. (2022). AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems. Global Journal of Medical Case Reports, 2(1), 1225. Retrieved from https://www.scipublications.com/journal/index.php/gjmcr/article/view/1225

Challa, S. R. (2022). Optimizing Retirement Planning Strategies: A Comparative Analysis of Traditional, Roth, and Rollover IRAs in LongTerm Wealth Management. Universal Journal of Finance and Economics, 2(1), 1276. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1276

Sikha, V. K. (2023). The SRE Playbook: Multi-Cloud Observability, Security, and Automation (Vol. 2, No. 2, pp. 2-7). SRC/JAICC-136. Journal of Artificial Intelligence & Cloud Computing DOI: doi. org/10.47363/JAICC/2023 (2) E136 J Arti Inte & Cloud Comp.

Sunkara, J. R., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., & Gollangi, H. K. (2023). Optimizing Cloud Computing Performance with Advanced DBMS Techniques: A Comparative Study. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3206

Sikha, V. K. Mastering the Cloud-How Microsoft's Frameworks Shape Cloud Journeys.

Kothapalli Sondinti, L. R., & Yasmeen, Z. (2022). Analyzing Behavioral Trends in Credit Card Fraud Patterns: Leveraging Federated Learning and Privacy-Preserving Artificial Intelligence Frameworks. Universal Journal of Business and Management, 2(1), 1224. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1224

Sikha, V. K. Building Serverless Solutions Using Cloud Services.

Venkata Narasareddy Annapareddy. (2022). Innovative Aidriven Strategies For Seamless Integration Of Electric Vehicle Charging With Residential Solar Systems. Migration Letters, 19(6), 1221–1236. Retrieved from https://migrationletters.com/index.php/ml/article/view/11618

Rajaram, S. K., Konkimalla, S., Sarisa, M., Gollangi, H. K., Madhavaram, C. R., Reddy, M. S., (2023). AI/ML-Powered Phishing Detection: Building an Impenetrable Email Security System. ISAR Journal of Science and Technology, 1(2), 10-19.

Sikha, V. K. Ease of Building Omni-Channel Customer Care Services with Cloud-Based Telephony Services & AI.

Kothapalli Sondinti, L. R., & Syed, S. (2021). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics, 1(1), 1223. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1223

Kannan, S. (2022). The Role Of AI And Machine Learning In Financial Services: A Neural Networkbased Framework For Predictive Analytics And Customercentric Innovations. Migration Letters, 19(6), 1205-1220.

Ganesan, P., Sikha, V. K., & Siramgari, D. R. TRANSFORMING HUMAN SERVICES: LEVERAGING AI TO ADDRESS WORKFORCE CHALLENGES AND ENHANCE SERVICE DELIVERY.

Patra, G. K., Rajaram, S. K., Boddapati, V. N., Kuraku, C., & Gollangi, H. K. (2022). Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security. International Journal of Engineering and Computer Science, 11(08), 25618–25631. https://doi.org/10.18535/ijecs/v11i08.4698

Sikha, V. K. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.

Vankayalapati, R. K., Edward, A., & Yasmeen, Z. (2022). Composable Infrastructure: Towards Dynamic Resource Allocation in Multi-Cloud Environments. Universal Journal of Computer Sciences and Communications, 1(1), 1222. Retrieved from https://www.scipublications.com/journal/index.php/ujcsc/article/view/1222

Malempati, M. (2022). Machine Learning and Generative Neural Networks in Adaptive Risk Management: Pioneering Secure Financial Frameworks. In Kurdish Studies. Green Publication. https://doi.org/10.53555/ks.v10i2.3718

Sarisa, M., Boddapati, V. N., Kumar Patra, G., Kuraku, C., & Konkimalla, S. (2022). Deep Learning Approaches To Image Classification: Exploring The Future Of Visual Data Analysis. In Educational Administration: Theory and Practice. Green Publication. https://doi.org/10.53555/kuey.v28i4.7863

Chava, K. (2023). Revolutionizing Patient Outcomes with AI-Powered Generative Models: A New Paradigm in Specialty Pharmacy and Automated Distribution Systems. In Journal for ReAttach Therapy and Developmental Diversities. Green Publication. https://doi.org/10.53555/jrtdd.v6i10s(2).3448

Kishore Challa,. (2022). Generative AI-Powered Solutions for Sustainable Financial Ecosystems: A Neural Network Approach to Driving Social and Environmental Impact. Mathematical Statistician and Engineering Applications, 71(4), 16643–16661. Retrieved from https://philstat.org/index.php/MSEA/article/view/2956

Sondinti, L. R. K., & Yasmeen, Z. (2022). Analyzing Behavioral Trends in Credit Card Fraud Patterns: Leveraging Federated Learning and Privacy-Preserving Artificial Intelligence Frameworks.

Sai Teja Nuka (2023) A Novel Hybrid Algorithm Combining Neural Networks And Genetic Programming For Cloud Resource Management. Frontiers in Health Informa 6953-6971

Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Gollangi, H. K. (2022). PREDICTING DISEASE OUTBREAKS USING AI AND BIG DATA: A NEW FRONTIER IN HEALTHCARE ANALYTICS. In European Chemical Bulletin. Green Publication. https://doi.org/10.53555/ecb.v11:i12.17745

Chakilam, C. (2022). Generative AI-Driven Frameworks for Streamlining Patient Education and Treatment Logistics in Complex Healthcare Ecosystems. In Kurdish Studies. Green Publication. https://doi.org/10.53555/ks.v10i2.3719

Maguluri, K. K., Yasmeen, Z., & Nampalli, R. C. R. (2022). Big Data Solutions For Mapping Genetic Markers Associated With Lifestyle Diseases. Migration Letters, 19(6), 1188-1204.

Eswar Prasad Galla.et.al. (2021). Big Data And AI Innovations In Biometric Authentication For Secure Digital Transactions Educational Administration: Theory and Practice, 27(4), 1228 –1236Doi: 10.53555/kuey.v27i4.7592

Komaragiri, V. B., & Edward, A. (2022). AI-Driven Vulnerability Management and Automated Threat Mitigation. International Journal of Scientific Research and Management (IJSRM), 10(10), 981-998.

Vankayalapati, R. K., & Syed, S. (2020). Green Cloud Computing: Strategies for Building Sustainable Data Center Ecosystems. Online Journal of Engineering Sciences, 1(1), 1229. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1229

Venkata Nagesh Boddapati, Eswar Prasad Galla, Janardhana Rao Sunkara, Sanjay Ramdas Bauskar, Gagan Kumar Patra, Chandrababu Kuraku, Chandrakanth Rao Madhavaram, 2021. "Harnessing the Power of Big Data: The Evolution of AI and Machine Learning in Modern Times", ESP Journal of Engineering & Technology Advancements, 1(2): 134-146.

Murali Malempati. (2022). AI Neural Network Architectures For Personalized Payment Systems: Exploring Machine Learning’s Role In Real-Time Consumer Insights. Migration Letters, 19(S8), 1934–1948. Retrieved from https://migrationletters.com/index.php/ml/article/view/11632

Vankayalapati, R. K., & Rao Nampalli, R. C. (2019). Explainable Analytics in Multi-Cloud Environments: A Framework for Transparent Decision-Making. Journal of Artificial Intelligence and Big Data, 1(1), 1228. Retrieved from https://www.scipublications.com/journal/index.php/jaibd/article/view/1228

Mohit Surender Reddy, Manikanth Sarisa, Siddharth Konkimalla, Sanjay Ramdas Bauskar, Hemanth Kumar Gollangi, Eswar Prasad Galla, Shravan Kumar Rajaram, 2021. "Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting", ESP Journal of Engineering & Technology Advancements, 1(2): 188-200.

Ganti, V. K. A. T., & Pandugula, C. Tulasi Naga Subhash Polineni, Goli Mallesham (2023) Exploring the Intersection of Bioethics and AI-Driven Clinical Decision-Making: Navigating the Ethical Challenges of Deep Learning Applications in Personalized Medicine and Experimental Treatments. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-230. DOI: doi. org/10.47363/JMSMR/2023 (4), 192, 1-10.

Chandrakanth R. M., Eswar P. G., Mohit S. R., Manikanth S., Venkata N. B., & Siddharth K. (2021). Predicting Diabetes Mellitus in Healthcare: A Comparative Analysis of Machine Learning Algorithms on Big Dataset. In Global Journal of Research in Engineering & Computer Sciences (Vol. 1, Number 1, pp. 1–11). https://doi.org/10.5281/zenodo.14010835

Sondinti, L. R. K., & Syed, S. (2022). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Finance and Economics, 1(1), 1223.

Karthik Chava. (2022). Redefining Pharmaceutical Distribution With AI-Infused Neural Networks: Generative AI Applications In Predictive Compliance And Operational Efficiency. Migration Letters, 19(S8), 1905–1917. Retrieved from https://migrationletters.com/index.php/ml/article/view/11630

Sarisa, M., Boddapati, V. N., Patra, G. K., Kuraku, C., Konkimalla, S., & Rajaram, S. K. (2020). An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods. Journal of Artificial Intelligence and Big Data, 1(1), 75–85. Retrieved from https://www.scipublications.com/journal/index.php/jaibd/article/view/1110

Nuka, S. T. (2022). The Role of AI Driven Clinical Research in Medical Device Development: A Data Driven Approach to Regulatory Compliance and Quality Assurance. Global Journal of Medical Case Reports, 2(1), 1275. Retrieved from https://www.scipublications.com/journal/index.php/gjmcr/article/view/1275

Gollangi, H. K., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Reddy, M. S. (2020). Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records. Journal of Artificial Intelligence and Big Data, 1(1), 65–74. Retrieved from https://www.scipublications.com/journal/index.php/jaibd/article/view/1109

Harish Kumar Sriram. (2022). AI Neural Networks In Credit Risk Assessment: Redefining Consumer Credit Monitoring And Fraud Protection Through Generative AI Techniques. Migration Letters, 19(6), 1237–1252. Retrieved from https://migrationletters.com/index.php/ml/article/view/11619

Manikanth Sarisa, Venkata Nagesh Boddapati, Gagan Kumar Patra, Chandrababu Kuraku, Siddharth Konkimalla, Shravan Kumar Rajaram.Navigating the Complexities of Cyber Threats, Sentiment, and Health with AI/ML. (2020). JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), 8(2), 22-40. https://doi.org/10.70589/JRTCSE.2020.2.3

Kumar Sriram, H. (2023). Harnessing AI Neural Networks and Generative AI for Advanced Customer Engagement: Insights into Loyalty Programs, Marketing Automation, and Real-Time Analytics. In Educational Administration: Theory and Practice. Green Publication. https://doi.org/10.53555/kuey.v29i4.9264

Gollangi, H. K., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Reddy, M. S. (2020).Unveiling the Hidden Patterns: AI-Driven Innovations in Image Processing and Acoustic Signal Detection. (2020). JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), 8(1), 25-45. https://doi.org/10.70589/JRTCSE.2020.1.3.

Harish Kumar Sriram, Dr. Aaluri Seenu. (2023). Generative AI-Driven Automation in Integrated Payment Solutions: Transforming Financial Transactions with Neural Network-Enabled Insights. International Journal of Finance (IJFIN), 36(6), 70–95.

Hemanth Kumar Gollangi, Sanjay Ramdas Bauskar, Chandrakanth Rao Madhavaram, Eswar Prasad Galla, Janardhana Rao Sunkara and Mohit Surender Reddy.(2020). “Echoes in Pixels: The intersection of Image Processing and Sound detection through the lens of AI and Ml”, International Journal of Development Research. 10,(08),39735-39743. https://doi.org/10.37118/ijdr.28839.28.2020.

Manikanth Sarisa, Venkata Nagesh Boddapati, Gagan Kumar Patra, Chandrababu Kuraku, Siddharth Konkimalla and Shravan Kumar Rajaram. “The power of sentiment: big data analytics meets machine learning for emotional insights”, International Journal of Development Research, 10, (10), 41565-41573.