Failure analysis of a secondary driving helical gear in transmission of electric vehicle

Harmonic drive is the core component of the industrial robot, and its fault diagnosis is crucial to the reliability and performance of the equipment. Most machine learning methods achieve good results based on the assumption of data balance. However, the scarce fault data of harmonic drive is difficult to collect, resulting in various imbalanced health status samples, which has an adverse effect on fault diagnosis. In this article, we propose a data generation method based on generative adversarial networks (GANs) to solve the problem of data imbalance and utilize the multiscale convolutional neural network (MSCNN) to realize the fault diagnosis of the harmonic drive. First, the data collected from three vibration acceleration sensors are preprocessed by fast Fourier transform (FFT) to obtain the frequency spectrum of the vibration signal. Second, multiple GANs were adopted to generate various fault spectrum data and the data selection module (DSM) is elaborately designed to filter and purify these data. Third, the filtered generated data will be combined with the real data to form a balanced dataset, and then the MSCNN is used to achieve multiclassification of the health status of the harmonic drive. Finally, the experiments have been implemented on an industrial robot vibration test bench to validate the effectiveness of our approach. The results have shown the fault multiclassification accuracy as 98.49% under imbalanced fault data conditions, which outperforms that of the other compared methods.

Fault Diagnosis of Harmonic Drive With Imbalanced Data Using Generative Adversarial Network

Harmonic drive is a key component in industry robot. Its complex design is highly sensitive to the manufacturing and assembly errors. Even a small error could cause excessive vibration jeopardizing the performance of the robot. Moreover, the vibration is dependent on the operation conditions. As a result, it is necessary to carry out a comprehensive test to detect possible manufacturing and assembling faults. This article presents an intelligent approach that can automatically identify different health conditions of harmonic drives. First, the acquired data from multiple acceleration sensors are cascaded in sequence after several preprocessing steps. Then, the multiscale convolutional neural network (MSCNN) architecture is used for disposing the inherent multiscale characteristics of the harmonic drive vibration signal, which can simultaneously perform multiscale feature extraction and classification. Finally, a large number of experiments were conducted on a real industrial robot vibration test platform to evaluate our approach. Based on the experiment results, the fault detection classification accuracy is 96.79%, which is higher than the other compared methods. This demonstrates that the presented method is effective for shop floor applications.

Fault Detection of Harmonic Drive Using Multiscale Convolutional Neural Network

Industrial robots play a key role in production lines. As a direct consequence, even a slight degradation of their operating conditions could negatively affect the entire manufacturing process. To minimize economical losses, preventive measures such as planned maintenance or stand-by working stations are adopted and, in some cases, additional manipulators are installed to ensure line availability. However, since each robot performs a specific task, it degrades at a different rate from another one, so Planned Preventive Maintenance (PPM) should be replaced with Condition Based Maintenance (CBM) for a more efficient and cost-effective approach. In addition, collaborative robots (cobots) have become more and more popular in the past years increasing the level of automation, in particular in small and medium size companies. Therefore, a failure of an industrial manipulator could not only cause unexpected downtimes, but also jeopardize the safety of the personnel with whom it shares its workspace. A cobot, in fact, is labeled as safe as long as it works in nominal conditions, but this cannot be guaranteed otherwise. Within this framework, Prognostics and Health Management (PHM) techniques could be used for both a customized maintenance on a specific machine and for preventing undesired events like the aforementioned ones. An ongoing research activity at Politecnico di Torino is focused on a model-based approach able to better describe the behavior of an industrial robot in non-nominal operating conditions. In order to properly simulate faults and failures of a manipulator, it has been first necessary to analyze its failure modes and their impact on the entire system. A robot, in fact, is usually equipped with internal sensors and algorithms able to monitor its health status. However, this is only true for software or electrical failures (i.e. power supply, motor or sensors related), but it is not as effective in case of mechanical ones like bearings or gearbox wear. This paper firstly presents an overview of the possible failures related to harmonic drives which are often used in robotics due to their compact and light-weight design and the high reduction ratio. Then, it outlines the work done with the objective of providing an accurate, physics-based description of faults progression of harmonic drives used in industrial robots and the related high-fidelity models in the framework of a PHM system for fault and failure detection, isolation and remaining useful life (RUL) prediction.

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Harmonic Drive Gear Failures in Industrial Robots Applications: An Overview

Flexible bearing is an important component of harmonic reducer which is widely used in automation equipment, especially in industrial robots. Studying its mechanical characteristics has great signi...

https://journals.sagepub.com/doi/pdf/10.1177/1729881420919953

Research on static and dynamics mechanical characteristics of flexible bearing in harmonic reducer

The failure process and failure mechanism of the flexspline in harmonic reducer system after 500h operation were analyzed. The microstructure, element composition and mechanical properties of flexspline were studied by optical microscope (OM), scanning electron microscope (SEM), atomic emission spectrometry (AES), infrared carbon sulfur analyzer and microhardness tester. The stress distribution of the key parts of the flexspline was calculated by LS-DYNA finite element simulation software. The results revealed that the main reason for the failure of the flexspline was the local micro crack and the variation of the dimensional accuracy, the essential reason was grain and ferrite phase inappropriately. In addition, the stress of the failed flexspline was concentrated near the crack, the maximum stress was increased by 26%, which compared well with empirical conclusion. This work can offer a reference for the further improvement of the flexspline.

https://iopscience.iop.org/article/10.1088/1757-899X/452/4/042148/pdf

Jianlin Zheng

Papers

Failure Analysis of a Flexspline of Harmonic Gear Drive in STC Industrial Robot: Microstructure and Stress Distribution