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International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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DESIGN AND IMPLEMENTATION OF HARDWARE BASED PREDICTIVE MAINTENANCE SYSTEM FOR HOME AUTOMATION MACHINES

AUTHORS:
Dhanuja S
Dharshini S
Madhumitha S
Mentor
Dr. B.Umarani
Affiliation
Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

improving the reliability, safety, and lifespan of household appliances by predicting failures before they occur. This system continuously monitors the operational parameters of home automation devices such as motors, Table fan, washing machines, and smart appliances using sensors. The collected data is processed using a microcontroller to detect abnormal behavior like excessive temperature, vibration, Current.


Based on predefined thresholds or predictive algorithms, the system alerts users in advance through visual indicators or mobile notifications. This approach reduces unexpected breakdowns, minimizes maintenance costs, and enhances energy efficiency, making home automation systems smarter and more reliable. This system improves appliance reliability, reduces unexpected failures, and extends the lifespan of home automation machines.

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S, D., S, D. & S, M. (2026). Design and Implementation of Hardware Based Predictive Maintenance System for Home Automation Machines. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.075

S, Dhanuja, et al.. "Design and Implementation of Hardware Based Predictive Maintenance System for Home Automation Machines." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.075.

S, Dhanuja,Dharshini S, and Madhumitha S. "Design and Implementation of Hardware Based Predictive Maintenance System for Home Automation Machines." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.075.

References
1.Alam M, Ali M and Reaz M (2012), “A Review of Smart Home Monitoring Technologies”, International Journal of Smart Home, Vol.6, No.3, pp.1-16.

2.Bagheri B, Kao H and Lee J (2015), “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems”, Manufacturing Letters, Vol.3, pp. 18-23.

3.Banjevic D, Jardine A K S and Lin D (2006), “A Review on Machinery Diagnostics and Prognostics”, Mechanical Systems and Signal Processing, Vol.20, No.7, pp.1483-1510.

4.Chen X, Gao R and Yan R (2014), “Wavelets for Fault Diagnosis of Rotating Machines”, Mechanical Systems and Signal Processing, Vol.25, No.7, pp. 2194-2209.

5.Dong M, Peng Y and Zuo M (2010), “Current Status of Machine Prognostics in Condition-Based Maintenance”, International Journal of Advanced Manufacturing Technology, 50, No.1-4, pp.297-313.

6.Goyal R and Kumar A (2021), “IoT-Based Smart Appliance Monitoring System”, International Journal of Engineering Research & Technology, Vol.10, No.5, pp.350-356.

7.Li X, Ding Q and Sun J (2018), “Remaining Useful Life Estimation in Prognostics Using Deep Learning Approach”, Reliability Engineering & System Safety, Vol.172, pp.1-11.

8.Mobley R K (2002), “An Introduction to Predictive Maintenance”, Butterworth-Heinemann Publications, Vol.2, pp. 1-320.

9.Peng Y, Dong M and Zuo M (2010), “Current Status of Machine Prognostics in Condition-Based Maintenance: A Review”, International Journal of Advanced Manufacturing Technology, Vol.50, pp.297-313.

10.Qin S J (2012), “Survey on Data-Driven Industrial Process Monitoring and Diagnosis”, Annual Reviews in Control, Vol.36, No.2, pp.220-234.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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