Survey of Gear Fault Diagnosis Using Various Statistical Signals Parameters

doi:10.21608/jest.2022.216105

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One disadvantage to these approaches was that they did not consider graduality for the fault conditions. From the previous works, it can be noted that the extraction of features is very common in the development of diagnosis methodologies because they provide useful information related to the fault conditions [21]. Nonetheless, if they are not correctly selected, they can lead to stagnation problems due to information redundancy or because no information contribution exists.…”

Section: Introductionmentioning

confidence: 99%

Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals

et al. 2023

View full text Add to dashboard Cite

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%