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In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. [ABSTRACT FROM AUTHOR]

Condition monitoring plays a vital role in predictive maintenance of machinery in today's world. It is very important to detect the faults in the machinery as early as possible in order to stop the propagation of faults which may lead to heavy damages. The objective of our study is to investigate the vibration analysis of bevel gearbox to detect the faults. An experimental setup was developed to carry out the vibration analysis. Various types of faults were induced in the gearbox. The vibration analysis was carried out for different types of faults with different lubrication levels in the gearbox. MATLAB toolbox was used for signal processing in which Coiflet wavelet was used for denoising the signal. An artificial neural network (ANN) and a deep neural network (DNN) were used to detect the faults in the bevel gearbox automatically. The results were promising in detecting the faults with high accuracy. [ABSTRACT FROM AUTHOR]

A rolling element bearing is a common component in household and industrial machines. Even a minor fault in this section has a negative impact on the machinery's overall operation. As a result, the industry suffers significant financial losses, and this damage can potentially result in catastrophic failures. Therefore, even a little fault in the rolling element bearings must be recognized and remedied as soon as possible. Many ways for detecting REB defects have been created in recent years, and new methods are being introduced on a daily basis. This article will provide a summary of such methods, with a focus on vibration analysis techniques. The newest advancements in this field will be recognizable to readers of this article. Anyone interested in defect diagnostics of rolling element bearings can utilize this material. [ABSTRACT FROM AUTHOR]

This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. [ABSTRACT FROM AUTHOR]

Journal bearings are widely used to support the shaft of industrial machinery with heavy loads, such as compressors, turbines and centrifugal pumps. The major problem in journal bearing is catastrophic failure due to corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. High frequency acceleration enveloping facilitates the extraction of low amplitude, high frequency signals associated with repetitive impacts in journal bearings, providing a key tool for early detection in the onset of bearing damage and similar machinery health problems when coupled with standard FFT analysis. The DEWESOFT software-based methods for implementing and interpreting high frequency acceleration enveloping are presented and compared. In this paper the application of STFT (Short Time Fourier Transform) and Autocorrelation through FFT are used for processing vibration signal to detect faults in journal bearing is presented. A bearing testing apparatus is used for experimental studies to obtain vibration signal from a healthy bearing and fault bearing. Further application of ANN for extracting features like RMS, mean, kurtosis, shape factor, crest factor and impulse factor is investigated as a model for automated fault diagnosis is presented. [ABSTRACT FROM AUTHOR]