Gearbox is an essential device employed in industries to vary speed and load conditions according to the requirements. More advancement in its design and operation leads to increase in industrial applications. The failure in any of the components of gearbox can lead to production loss and increase maintenance cost. The component failure has to be detected earlier to avoid unexpected breakdown. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. This paper addresses the use of vibration signal for automated fault diagnosis of gearbox. In the experimental studies, good gears and face wear gears are used to collect vibration signals for good and faulty conditions of the gearbox. Each gear is tested with two different speeds and loading conditions. The statistical features are extracted from the acquired vibration signals. The extracted features are given as an input to the support vector machine (SVM) for fault identification. The Performance of the fault identification system using vibration signals are discussed and compared

A gear box is widely employed in automobiles and industrial machines for transmitting power and torque. It operates under various working conditions for prolonged hours increasing the chance of gearbox failure. Major faults in gear systems are caused due to wear, scoring, pitting, tooth fracture, etc. Gear box failure leads to increases in machine downtime and maintenance costs. The nature and location of such failures can be identified with precision using condition monitoring techniques. In this study, machine condition data are acquired from the gear box using a vibration accelerometer, microphone and acoustic emission sensors under different operating conditions, such as three loading conditions (0 N, 5 N, 10 N) and three rpm variations (500, 750, 1000). The wavelet features are extracted from the acquired vibration, sound and acoustic emission signal, and prominent features are identified. To automate the process of fault diagnosis, machine learning algorithms (artificial neural network, support vector machine, and proximal support vector machine) are utilized. Dual and multi-sensor fusion is implemented with the help of prominent features, to intensify the classification accuracy. The performance of the individual signals, and dual and multi-sensor fused models in gearbox fault diagnosis are compared and discussed in detail.

The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.

Rotational machines in industries often encounter uncertainties during operation, are monitored and diagnosed through machine condition monitoring. Particularly when speed varies, Artificial Intelligence (AI) provides a great deal of support in recognizing machine faults efficiently. This paper reviews various articles that focused on fault identification in industrial rotating machines under varying speed condition using AI techniques. It investigates various machinery faults, experimental datasets and reputed institutions datasets, complex signals from faulty components and their processing method. Further, the involvement of intelligent based techniques in extracting and selecting suitable features to classify defects are comprehensively reviewed. In addition, as multi-signal fusion strategy is evolving recently for fault diagnosis, the articles comprised of this tactic are also discussed. The objective of this article is to provide a comprehensive understanding of these scenarios to the institutions, industries and researchers, thereby facilitating further exploration.