Helicopter Bolt Loosening Monitoring using Vibrations and Machine Learning

Abstract

The existing helicopter Health and Usage Management Systems (HUMS) collect and process flight operational parameters and sensors data such as vibrations to provide health monitoring of the helicopter dynamic assemblies and engines. So far, structure-related mechanical faults, such as looseness in bolted structures, have not been addressed by vibration-based condition monitoring in existing HUMS systems. Bolt loosening was identified as a potential risk to flight safety demanding periodical visual monitoring, and increased maintenance and repair expenses. Its automatic identification in helicopters by using vibration measurements is challenging due to the limited number of known events and the presence of high-energy vibrations originating in rotating parts, which shadow the low-level signals generated by the bolt loosening.
 New developed bolt loosening monitoring approach was tested on HUMS vibrations data recorded from the IAF AH-64 Apache helicopters fleet. ML-based unsupervised anomaly detection was utilized in order to address the limited number of faulty cases. The predictive power of health features was significantly improved by applying the Harmonic filtering differentiating between the high-energy vibrations generated by rotating parts compared with the low-energy structural vibrations. Different unsupervised anomaly detection techniques were examined on the dataset. The experimental results demonstrate that the developed approach enable successful bolt loosening monitoring in helicopters and can potentially be used in other health monitoring applications.

Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

“…They classified the faults into three types: inner race faults, outer race faults and rolling element faults. Gildish et al [19] evaluated three regression approaches: Ridge, support vectors, and deep learning regression and determined to provide accurate predictions of operational conditions. The experiments were carried out on ten datasets, with half of the data used for training and the other half for testing.…”

“…Gildish et al [19] have compared the performance of three machine learning regressors in their paper: Ridge, support vector regressor, and deep learning regression. The support vector regressor yielded excellent results.…”

Vibration Signal Decomposition using Dilated CNN

“…The first source is generated by sub-systems, such as gears, that are phaselocked to the operating speed, resulting in periodic components within the signal (periodic or deterministic part of signal). The second source comprises components that are not phase-locked (broadband or non-deterministic part of signal), such as bearings that experience rolling and slipping due to varying loads, as well as structure-related vibrations, as demonstrated by Gildish at al. (2022, June).…”

“…An alternative approach to extracting periodic contents is proposed by Groover et al (2005), Braun (2011), Peeters et al (2005 and2007) and in Gildish at al. (2022 June).…”

An In-Depth Study of Vibration Sensors for Condition Monitoring

“…Predictive maintenance helps prevent the occurrence of these breakdowns by predicting possible problems before they become catastrophic failures. These machines are equipped with sensors [25][26][27][28][29] that continuously collect data on parameters like temperature [9,20,25,30], pressure [7,20] and vibration [21]. These data are then evaluated by software, which uses cutting-edge methods such as machine learning [31], previous data, and contextual information to construct predictive models that identify expected issues or maintenance requirements based on usage patterns and environmental factors.…”