Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 516-521· 0 citations· 15 references
Abstract
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
The fast changing nature of connected vehicles requires smart systems that can monitor health in real-time and predict failures. This paper introduces an IoT-based Smart Vehicle Monitoring and Predictive Maintenance System that involves onboard sensors, edge computing, and cloud analytics to ensure high reliability and reduce unscheduled downtimes. Multi-modal transportation data (temperature, vibration, fuel consumption, and brake parameters) are continuously measured with the help of ESP32/Arduino devices connected to the CAN bus. An Extended Kalman Filter (EKF) is an algorithm that does nonlinear sensor fusion at the edge to enhance data reliability and noise minimization. The degradation prediction and Remaining Useful Life estimation of the fused time-series data are done with a Long Short-Term Memory (LSTM) network. A new adaptive thresholding system is used to dynamically regulate anomaly sensitivity according to driving context and historical trends. It was evaluated experimentally on 100,000 real-time sample divided into 70% training, 15% validation and 15% testing sample. The proposed framework was found to have 97.4% prediction accuracy with Precision (94.8%), Recall (95.6%), and F1-score (95.2%). EKF preprocessing minimized RMSE by 0.84, and it is a 56-percent improvement in estimation accuracy. The adaptive detection module reduced false positive rate to 4.1% which was 63% lower than the approaches to static threshold. The system forecasted failures almost 22 minutes earlier than it was actually detected which enhanced early detection by almost 30 percent. The edge deployment decreased latency by a factor of 4 to 160 ms, and thus, allowed the creation of nearly real-time alerts. The implementation of visualization and alert management was realized based on the use of Node-RED and Grafana dashboards, along with MQTT-based secure communication. All in all, the framework is statistically proven to be robust, scaled and applicable to next-generation intelligent vehicular IoT ecosystems.
Biswaraj Roy, B. Prasath, Ajit Kumar Singh et al.· 2026 6th International Confe...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety hazards. To address these challenges, this paper presents the design and implementation of an Internet of Things (IoT)-enabled real-time machine health monitoring and protection system based on the ESP32 microcontroller platform. The proposed system integrates a DHT11 sensor for temperature and humidity monitoring, an ACS712 Hall-effect sensor for current measurement, and an MPU9250 inertial measurement unit (IMU) for vibration analysis. Sensor data are continuously acquired, processed, and transmitted through Wi-Fi to a cloud-based Firebase Realtime Database, enabling remote access and centralized monitoring. A responsive web dashboard hosted on GitHub Pages provides real-time visualization of machine operating parameters, status indicators, and fault notifications. To enhance operational safety and equipment reliability, threshold-based fault detection algorithms are implemented to identify abnormal operating conditions. When predefined critical limits are exceeded, the ESP32 automatically initiates protective actions by disconnecting the machine through a relay module, activating a visual alarm, and updating the fault status on the cloud platform. The dashboard additionally supports bidirectional communication, allowing authorized operators to remotely restart the machine, while a local push-button interface enables manual system recovery. Furthermore, the developed platform incorporates a browser-based logging mechanism that records timestamped sensor measurements, machine status transitions, fault events, and downloadable CSV trend data for maintenance analysis and performance evaluation. Experimental validation demonstrates reliable real-time monitoring with a data refresh interval of approximately 3 s, accurate threshold-based fault detection, dependable cloud connectivity, and effective remote supervisory control. The proposed solution offers a low-cost, scalable, and practical framework for predictive maintenance and industrial equipment condition monitoring in smart manufacturing environments.
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
Intelligent monitoring and control of modern microgrids have become essential to increase the efficiency, reliability and fault tolerance of the microgrid. This research work proposes an autonomous microgrid management system with AI-powered load and fault prediction that combines renewable energy sources, IoT and cloud computing in one embedded system. The proposed system encompasses an ESP32 microcontroller that collects the values of voltage and current using sensors, calculates electrical parameters, and transfers data to the cloud database (Firebase) via Wi-Fi connection. A web dashboard is used to provide real-time monitoring of the system, while a machine learning algorithm is used to predict future load and detect abnormal states for proactive fault management. An automatic relay-based load control scheme is applied to protect the system from abnormal load faults. Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms. The proposed AI prediction model has achieved 95.3% accuracy, proving the feasibility of the proposed system design.
Chellan P., D. M., Sivasubramani. G. et al.· Journal of Electrical Engine...· 0 citations
Three-phase induction motors are extensively utilized in industrial and commercial applications due to their robustness, efficiency, and capability to handle high-power loads. However, these motors are vulnerable to damage from phase failure conditions, including single-phasing, voltage imbalance, and phase loss, which can lead to overheating, reduced efficiency, and permanent equipment failure. This paper presents the design and development of a low-cost, intelligent three-phase monitoring and protection system integrated with Internet of Things (IoT) communication for real-time fault notification. The proposed system employs an Arduino Uno microcontroller as the central processing unit, continuously monitoring the availability of R, Y, and B phases through voltage sensor modules. Upon detection of any phase failure, the system automatically disconnects relay-controlled loads to prevent single-phasing damage. Local status indication is provided through an I2C LCD display and audible buzzer alerts, while remote monitoring is achieved via an ESP32 DevKit module that transmits fault notifications to a Telegram application through Wi-Fi connectivity. The system architecture combines embedded control, automated protection, and wireless communication into a single scalable platform. Experimental validation demonstrates reliable phase detection, rapid relay response, and effective remote alerting, confirming the system's suitability for industrial automation, motor protection, and smart energy management applications.
J. Babu, More Divya, Varu Chirag et al.· International Journal for Sc...· 0 citations
