Investigation of Root Causes Analysis, Preventive Maintenance Delays & Breakdowns in Diesel Generators Using IOT Integrated Hardware & FEA Method

Abstract

This study investigates the root cause of preventive maintenance in diesel generators by integrating IoT-based monitoring and Finite Element Analysis (FEA). Using an ESP8266 controller with temperature sensors, vibration sensors, and an accelerometer, real-time data is collected to analyse temperature variations, vibration occurrences, and potential misalignment effects. Additionally, FEA is performed in ANSYS for different shaft rotational speeds, evaluating frequencies with modal analysis. A 3D model is developed in CATIA to simulate modal and thermal responses, aiding in identifying breakdown causes and optimizing generator performance for enhanced reliability and lifespan.

Introduction

With rising industrial demand for uninterrupted power, ensuring the reliability and efficiency of diesel generators has become crucial. Traditional time-based maintenance often leads to costly breakdowns and downtime. This study proposes an intelligent condition-based maintenance approach by integrating IoT real-time monitoring and advanced Finite Element Analysis (FEA) to diagnose failure causes in diesel generators.

Using an ESP8266 microcontroller connected to temperature, vibration, and accelerometer sensors, the system continuously tracks key operational parameters to detect early signs of overheating, misalignment, or mechanical faults. Real-time data is transmitted wirelessly for analysis, enabling prompt alerts for preventive maintenance.

FEA simulations in ANSYS, supported by a detailed 3D CAD model created in CATIA, analyze the generator shaft's structural and modal behavior under different rotational speeds. These simulations identify critical stress zones, resonance frequencies, and thermal responses that correlate with sensor data to pinpoint root causes of failures.

Experimental results show that vibration frequency and temperature increase with mode number and RPM, with higher speeds causing significantly more mechanical stress and heat generation. Notably, certain low-frequency modes cluster close together, indicating potential resonance risks.

The integrated IoT-FEA framework offers a predictive maintenance strategy aimed at optimizing generator performance, extending equipment lifespan, and minimizing unplanned downtime and repair costs by shifting from traditional scheduled maintenance to data-driven condition monitoring.

Conclusion

The vibration frequency and surface temperature were recorded across ten operational modes of a diesel generator at two rotational speeds: 500 RPM and 1000 RPM, over three consecutive experimental trials. The purpose of this comparative analysis is to observe consistency, identify deviations, and assess performance trends that support preventive maintenance planning. A. Vibration Frequency Trends At both RPM levels, the vibration frequency exhibited a clear increasing trend with each successive mode, indicating that higher mechanical modes are associated with greater vibrational activity. At 500 RPM, the first trial recorded a frequency range from 473 Hz (Mode 1) to 3753 Hz (Mode 10). Subsequent trials showed slightly reduced frequencies, ranging from 465 Hz to 3715 Hz (Trial 2) and 459 Hz to 3679 Hz (Trial 3). The reduction, although minor (typically 1–3%), reflects normal variability and potentially improved damping characteristics due to thermal stabilization or reduced system stress. At 1000 RPM, the frequency values were significantly higher, starting from 675 Hz (Mode 1) and peaking at 4789 Hz (Mode 10) in Trial 1. As with the lower RPM case, a consistent but slightly decreasing trend was noted in Trials 2 and 3, with values tapering to 667–4741 Hz and 660–4695 Hz, respectively. These reductions in vibration across trials suggest that the generator system may settle or stabilize slightly after initial operation, possibly due to lubricant distribution or thermal expansion balancing mechanical components. B. Temperature Trends Temperature measurements also followed an upward trend with increasing mode number at both RPMs. For 500 RPM, the first trial showed temperatures rising from 39°C in Mode 1 to 79°C in Mode 10. Trials 2 and 3 exhibited marginally lower readings, ranging from 38°C to 78°C and 37°C to 77°C, respectively. This pattern suggests that the system retains heat but demonstrates minor reductions in temperature with successive operations, possibly due to better heat dissipation or less friction as moving parts achieve optimal alignment. Similarly, at 1000 RPM, the temperatures were higher overall due to increased mechanical stress and internal heating. Initial readings started at 43°C, escalating to 87°C by Mode 10 in the first trial. In Trials 2 and 3, temperatures ranged from 42°C to 86°C and 41°C to 85°C, respectively. These differences are small but consistent, indicating system reproducibility and reliability of the ITO-based measurement setup. The slightly lower temperatures in later trials may also point to reduced surface friction and improved cooling effect after initial warm-up phases.

References

[1] Awoyemi, O. (Year). Impact of Maintenance on Machine Reliability: A Review. https://www.academia.edu/116846818/Impact_of_Maintenance_on_Machine_Reliability_A_Review [2] Muduli, K. (Year). Effect of Preventive Maintenance on Machine Reliability in a Beverage Packaging Plant. [3] Various Authors (Year). From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry. https://pmc.ncbi.nlm.nih.gov/articles/PMC10346720/ [4] Various Authors (Year). Inherent Factors Affecting the Effectiveness of Maintenance Using Root Cause Analysis: A Case Study. https://www.academia.edu/83268741 [5] Various Authors (Year). The Relationship Between Recurring Plant Failures and Load Shedding. https://www.academia.edu/120479812/The_relationship_between_reoccurring_plant_failures_and_load_shedding [6] Kumar, S. N., & Yogish, H. (Year). Preventive Maintenance and Breakdown Reduction of Critical Machines. https://www.academia.edu/34689662/Preventive_Maintenance_and_Breakdown_Reduction_of_Critical_Machines [7] Humaizi, A. (Year). Enhancing Predictive Maintenance in Diesel Generators for Industry 4.0. [8] Alghamdi, F. N. (Year). Enhancing Generator Preventive Maintenance: Strategies for Reliability and Efficiency. https://www.researchgate.net/publication/374505728_Impact_of_Maintenance_on_Machine_Reliability_A_Review [9] Various Authors (Year). Preventive Maintenance Scheduling Optimization: A Review of Applications for Power Plants. ScienceDirect. [10] Various Authors (Year). Application of Condition-Based Maintenance for Electrical Generators Based on Statistical Control Charts. [11] https://link.springer.com/chapter/10.1007/978-3-319-49938-3_24 [12] https://pmc.ncbi.nlm.nih.gov/articles/PMC10491639/

Copyright

Copyright © 2025 Tejas B. Bhujabal, Arnav R. Kulkarni, Ambadas R. Karadkhedkar, Suyash S. Mangire. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.