International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 4, August 2023, pp. 4467~4476 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4467-4476  4467 Journal homepage: http://ijece.iaescore.com Internal combustion engine gearbox bearing fault prediction using J48 and random forest classifier Nithin Somehalli Kapanigowda 1 , Hemanth Krishna 1 , Shamanth Vasanth 1 , Ananthapadmanabha Thammaiah 2 1 School of Mechanical Engineering, REVA University, Bangalore, India 2 School of Engineering, Mysore University, Mysuru, India Article Info ABSTRACT Article history: Received Sep 13, 2022 Revised Feb 1, 2023 Accepted Feb 4, 2023 Defective bearings in four-stroke engines can compromise performance and efficiency. Early detection of bearing difficulties in 4-stroke engines is critical. Four-stroke gasoline engines that vibrate or make noise can be used to diagnose issues. Using time, frequency, and time-frequency domain approaches, the vibrational features of healthy and diseased tissues are examined. Problems are only detectable by vibration or sound. The fault is identified through statistical analysis of seismic and audio data using frequency and time-frequency analysis. Vibration must be minimized prior to examination. Adaptive noise cancellation removes unwanted noise from recorded vibration signals, boosting the signal-to-noise ratio (SNR). In the first of the experiment's three phases, vibrational data are collected. To reduce noise and boost SNR, adaptive noise cancellation (ANC) is applied to vibration data from the first stage. In the second stage, ANC-filtered vibration data is subjected to three studies to detect bearing failure using J48 and random forest classifiers for online, real-time monitoring. In this experiment, one healthy and two faulty bearings are used. According to a current study, the internet of things (IoT) is a promising alternative for online monitoring of remote body health. Keywords: Artificial neural network Bearing Internet of things Machine learning classifier Petrol engine Random forest This is an open access article under the CC BY-SA license. Corresponding Author: Nithin Somehalli Kapanigowda School of Mechanical Engineering, REVA University Bangalore, India Email: nithinsk67@gmail.com 1. INTRODUCTION One of the essential machine parts in mechanical parts such as centrifugal pumps, turbines, and engine gearboxes. is the ball bearing. Bearings in internal combustion (IC) engine gearboxes play an important role in carrying the shaft and spinning at different speeds depending on the engine's operating conditions. The gearbox's vibration signals reveal features of the bearing's state. Many researchers rely extensively on vibration analysis while trying to diagnose problems with IC engines and other machinery. With the increased use of computing technology and its upcoming advancement in conjunction with the internet of things (IoT), as well as data science algorithms to online monitor and predict the health of mechanical equipment, in recent years, several sensors, such as those relating to vibration, acceleration, temperature, and air pressure, have been employed, or multiple sensors of the same type have been combined, to collect real-time operational status data pertaining to various mechanical equipment components. The research in the area of IC engine and rotating machine condition monitoring is described in the following publications. The literature has a great deal of work on failure detection and diagnostics in rolling element bearings. Nithin et al. [1] illustrate the applicability of the continuous wavelet transform (CWT) for