A One-Dimensional Convolutional Neural Network-Based Method for Diagnosis of Tooth Root Cracks in Asymmetric Spur Gear Pairs

Abstract

Gears are fundamental components used to transmit power and motion in modern industry. Their health condition monitoring is crucial to ensure reliable operations, prevent unscheduled shutdowns, and minimize human casualties. From this standpoint, the present study proposed a one-dimensional convolutional neural network (1-D CNN) model to diagnose tooth root cracks for standard and asymmetric involute spur gears. A 6-degrees-of-freedom dynamic model of a one-stage spur gear transmission was established to achieve this end and simulate vibration responses of healthy and cracked (25%–50%–75%–100%) standard (20°/20°) and asymmetric (20°/25° and 20°/30°) spur gear pairs. Three levels of signal-to-noise ratios were added to the vibration data to complicate the early fault diagnosis task. The primary consideration of the present study is to investigate the asymmetric gears’ dynamic characteristics and whether tooth asymmetry would yield an advantage in detecting tooth cracks easier to add to the improvements it affords in terms of impact resistance, bending strength, and fatigue life. The findings indicated that the developed 1-D CNN model’s classification accuracy could be improved by up to 12.8% by using an asymmetric (20°/30°) tooth profile instead of a standard (20°/20°) design.

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

“…Su et al [4] proposed a method for extracting images based on visual saliency regions in order to obtain more effective features. Based on FT [5], the method utilized Otsu thresholding to eliminate interference from non-detection areas, ineffective features, and edge burrs in the detection results.…”

“…In modern industries, Metal gears are used by fundamental components to transmit power and motion [5]. The presence of defects, such as tooth fractures and scratches, can lead to issues problems, which include noise generation and wear and so on during the motion of the gear.…”

“…1) To address the issue of non-detection areas affecting accuracy, this study proposes a defect region extraction algorithm called SF, which is based on the Frequency Tuning (FT) method [5], which in turn reduces the interference and complexity of the detected image, in order to reducing the difficulty of feature extraction.…”

Optimizing the Neural Network Architecture for Automation of the Tailored UV Post-Treatment of Photopolymer Printing Plates

“…Many previous works used ANNs for modeling various surface and other material properties. In [19], authors use ANNs to analyze cracks in gear teeth. In [20], ANNs were used to predict tribological performance of surface treated tool steels.…”

A Data-Driven Approach to Estimate the Power Loss and Thermal Behaviour of Cylindrical Gearboxes under Transient Operating Conditions

“…To describe and represent complex nonlinear problems, one can consider deep neural networks (DNNs) that contain multiple hidden layers [11]. Recently, ML techniques are employed in geared systems to predict whine noise generation [12], estimate power loss [13], detect root cracks [14], and forecast faults [15]. Moreover, ML has already been used in tribology from wear predictions [16] to tribodynamic simulation of machine elements [17], where the numerical approach of solving partial differential equations [18] is used to provide a training set for a neural network that can be used in fast and precise guessing.…”