Abstract
As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts.
Highlights
The gearbox is widely used for mechanical power transmission in industries such as petroleum equipment, mining machines, chemical industry, and railway applications [1,2]
Evaluation with infrared thermal (IRT) Images from Specified Temperature Ranges In Scenario 2, the performance of the proposed IRT–convolutional neural network (CNN) method is first examined under six small temperature ranges, each of them covering an interval of 3 ◦ C, to investigate the influences of temperature fluctuations
This paper develops a novel method for the condition monitoring (CM) of gearboxes using IRT images with CNN
Summary
The gearbox is widely used for mechanical power transmission in industries such as petroleum equipment, mining machines, chemical industry, and railway applications [1,2]. A variety of signals have been investigated for the condition monitoring (CM) of gearboxes [5,6,7,8,9], including vibration signal [10,11,12], current signal [9], acoustic emission signal [13,14,15], sound signal [16], torque signal [17], and rotating encoder signal [18] and so on. Among these sensor signals, Sensors 2019, 19, 2205; doi:10.3390/s19092205 www.mdpi.com/journal/sensors. Significant progress has been made in the search for an alternative sensing technique to monitor the health condition of gearboxes
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