Helicopter main gearbox bearing defect identification with acoustic emission techniques

Duan, Fang; Elasha, Faris; Greaves, Matthew; David, Éric

doi:10.1109/icphm.2016.7542856

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…As a result, the vibration data is processed using several signal-processing algorithms to determine which of the acquired data is the most sensitive. Several signal processing techniques, including statistical techniques, time-domain averaging, cepstrum estimation, Wigner-Viller distribution, demodulation [12], independent or principal component analysis cyclo-stationarity analysis, wavelet transforms [13] and EMD [14] have been utilised for signal processing. To overcome the disadvantages of the wavelet transform method, EMD was introduced.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Reddy Mutra,

D Mallikarjuna Reddy,

Amarnath

et al. 2023

Int. J. Automot. Mech. Eng.

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This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Reddy Mutra,

D Mallikarjuna Reddy,

Amarnath

et al. 2023

Int. J. Automot. Mech. Eng.

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show abstract

“…Compared with the vibration signal analysis method, the AE signal is more sensitive, less affected by the mechanical background noise, and can diagnose weak faults. Therefore, the method of fault diagnosis based on the AE signal has been widely used [ 8 , 9 ]. However, AE signal acquisition requires a very high sampling frequency and generates a large amount of redundant data.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Yang

Liu

et al. 2022

Sensors

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The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults.

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“…Tan et al, 2007) and it is difficult to see it implemented in commercial tools. In the area of HUMS some research has been carried out in recent years to prove the capabilities of AE to monitor helicopter transmission components, focusing on epicyclic gearboxes (Duan et al, 2015;Elasha et al, 2017;. These investigations concluded that AE offered much earlier indication of damage than vibration analysis, and the proposed processing techniques were suitable for gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of a Broadband Vibration and Acoustic Emission Sensor for Rotorcraft Transmission Monitoring

2022

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Modern rotorcrafts rely on Health and Usage Monitoring Systems (HUMS) to enhance their availability, reliability, and safety. In those systems, data related to the health of key mechanical components is acquired, in addition to typical flight condition history data such as speed and torque. Commercial HUM systems usually rely on vibration measurements to assess the condition of shafts, gears, and bearings; using techniques such as spectral analysis, harmonic analysis, vibration trend and others. Recent research has shown that acoustic emissions (AE) can be advantageous in the detection of mechanical faults, in particular detecting very early small defects on bearings and gears, providing extra time for maintenance planning. However, the addition of extra sensors adds complexity and weight to the HUMS system, which is undesirable. This research is an experimental study to assess the monitoring capabilities of a broadband sensor, able to cover both low frequency vibration components as well as ultrasonic events, hence combining the benefits of both in a single compact sensing unit. The experimental results obtained from an instrumented rig using healthy components as well as seeded faults show the ability of the sensor to detect high frequency events, and compares the performance of the sensor in the low frequency range with a commercial accelerometer.

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Helicopter main gearbox bearing defect identification with acoustic emission techniques

Cited by 6 publications

References 9 publications

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Experimental Assessment of a Broadband Vibration and Acoustic Emission Sensor for Rotorcraft Transmission Monitoring

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