A Review of Methodologies Used for Fault Diagnosis of Gearbox

Han, Zhen; Liu, Yu Long; Jin, Hong; Fu, Hong

doi:10.4028/www.scientific.net/amm.415.510

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…But this process is not an easy task. The first step of fault diagnosis for most mechanical equipment is signal acquisition [26]. The vibration signal collected by contact accelerometer and the acoustic signal collected by non-contact microphone are both not just the working signal of gear.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

Pan,

Ding,

Qiao

et al. 2023

Smart Mater. Struct.

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It is widely known that the gear fault signal is affected by the interference of strong background noise and the loss of internal fault excitation transmission, which weakens the features of incipient fault signals and makes gear fault diagnosis difficult. Motivated by the acoustic manipulation capabilities of acoustic metamaterials and the gear modulation principle, this study proposes a front-end perception method for weak gear fault feature diagnosis via gradient acoustic metamaterials (GAM), which utilizes the acoustic rainbow capture and compression ability to reduce the difficulty of gear incipient fault diagnosis. Considering the FM/AM modulation principle, acoustically enhanced sensing for fault features can be achieved by collecting components from the selected air gaps in the GAM structure. The feasibility of GAM-based gear weak fault feature diagnosis is proved by experiments, which illustrate the multiscale feature denoising and frequency-selective enhancement characteristics of GAM. The results show that the amplitude of the target signal is amplified by more than eight times, the sideband component containing the fault signal is clearly enhanced, and the effect of denoising outside the target sideband is evident. This allows the weak fault features to be easily identified. Compared to digital filtering in traditional post-processing methods, the proposed approach is more straightforward for extracting weak fault features. Additionally, this method adopts a non-contact measurement method with the MEMS microphone for front-end enhancement and sensing. Finally, it can be seen that the proposed method has great potential for structurally enhancing the perception of gearbox weak fault signal denoising and feature diagnosis.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

Pan,

Ding,

Qiao

et al. 2023

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…In fact, in [17], we already pointed out the possibility of overcoming limits related to bearing fault diagnosis under non-stationary conditions using MTA instead of MCSA. Furthermore, in [18], a review regarding the detection and diagnosis methodologies for gearboxes, it is demonstrated that the most useful signals for diagnosis are currents, torques, or vibrations. Since the manipulators' joints are composed of electric drives and speed reducers also for these cobots, the same considerations above may be considered valid.…”

Section: Related Workmentioning

confidence: 99%

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

2022

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The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.

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Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

et al. 2021

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A Review of Methodologies Used for Fault Diagnosis of Gearbox

Cited by 5 publications

References 19 publications

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

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