Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

2019 ◽  
Vol 41 (6) ◽  
Author(s):  
Pauline Ong ◽  
Tony Hieng Cai Tieh ◽  
Kee Huong Lai ◽  
Woon Kiow Lee ◽  
Maznan Ismon
Keyword(s):  
Feature Selection ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Wavelet Packet Analysis ◽  
Gear Fault

scholarly journals New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091659 ◽  
Author(s):  
Adel Afia ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Boualem Merainani ◽  
Semcheddine Fedala
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Radial Basis ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases.

Application of Wavelet Analysis to Fault Diagnosis of Mechanical Equipment

2012 ◽  
Vol 590 ◽  
pp. 325-328
Author(s):  
Shan Zhen Xu ◽  
Cheng Wang
Keyword(s):  
Wavelet Analysis ◽  
Simulation Analysis ◽  
Wavelet Packet ◽  
Gear Transmission ◽  
Good Time ◽  
Mechanical Equipment ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Adaptive Ability

Aiming at the characteristics of mechanical gear transmission, taking automobile main reducer as research object, this paper analyzes the gear transmission of failure mechanism and failure characteristics. According to the good time frequency character and adaptive ability of wavelet analysis, it proposes gear fault information extraction method based on wavelet packet analysis and have carried out simulation analysis. The results show that the method of wavelet packet analysis can effectively detect mutations in the signal part and noise to achieve the diagnosis of mechanical system failures.

scholarly journals Gear fault diagnosis using Autogram analysis

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881253 ◽  
Author(s):  
Adel Afia ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Modern Approach ◽  
Vibration Signals ◽  
Discrete Wavelet Packet Transform ◽  
Gear Fault ◽  
Data Signal ◽  
Non Gaussian ◽  
Non Stationary Signal

Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness.

Gear Fault Identification Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

2011 ◽  
Vol 110-116 ◽  
pp. 2562-2569
Author(s):  
Ali Soleimani
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wavelet Packet ◽  
Feature Selection Method ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Gear Fault ◽  
Artificial Neural

This study compares the performance of gear fault detection using Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The feature vector is extracted from vibration signals by standard deviation of wavelet packet coefficients at the fourth level. An improved distance evaluation technique is proposed for feature selection, and with it, the four salient features are selected from the original feature set. These features are normalized in the range zero to one and then fed into ANN and ANFIS. The gear conditions were considered to be healthy gearbox, slightly worn, and medium worn and broken-teeth gears faults. The output layer of ANN and ANFIS consists of one node indicating the status of the gearbox by four labels. A 3-layer Multi-Layer Perceptron (MLP) neural-network and an ANFIS were designed to carry out the task. The results show that the classification accuracy of ANFIS is better than MLP. Also, the effectiveness of the proposed feature selection method is demonstrated by the test results.

Real-time localization of micro-damage in concrete beams using DIC technology and wavelet packet analysis

2021 ◽  
pp. 104198
Author(s):  
Kaiyang Zhou ◽  
Dong Lei ◽  
Jintao He ◽  
Pei Zhang ◽  
Pengxiang Bai ◽  
...  
Keyword(s):  
Real Time ◽  
Concrete Beams ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Damage In Concrete

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

FUSION OF SURVEILLANCE IMAGES IN INFRARED AND VISIBLE BAND USING CURVELET, WAVELET AND WAVELET PACKET TRANSFORM

2010 ◽  
Vol 08 (02) ◽  
pp. 271-292 ◽  
Author(s):  
PARUL SHAH ◽  
S. N. MERCHANT ◽  
U. B. DESAI
Keyword(s):  
Wavelet Packet ◽  
Curvelet Transform ◽  
Visual Quality ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images

This paper presents two methods for fusion of infrared (IR) and visible surveillance images. The first method combines Curvelet Transform (CT) with Discrete Wavelet Transform (DWT). As wavelets do not represent long edges well while curvelets are challenged with small features, our objective is to combine both to achieve better performance. The second approach uses Discrete Wavelet Packet Transform (DWPT), which provides multiresolution in high frequency band as well and hence helps in handling edges better. The performance of the proposed methods have been extensively tested for a number of multimodal surveillance images and compared with various existing transform domain fusion methods. Experimental results show that evaluation based on entropy, gradient, contrast etc., the criteria normally used, are not enough, as in some cases, these criteria are not consistent with the visual quality. It also demonstrates that the Petrovic and Xydeas image fusion metric is a more appropriate criterion for fusion of IR and visible images, as in all the tested fused images, visual quality agrees with the Petrovic and Xydeas metric evaluation. The analysis shows that there is significant increase in the quality of fused image, both visually and quantitatively. The major achievement of the proposed fusion methods is its reduced artifacts, one of the most desired feature for fusion used in surveillance applications.

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