Shock and Vibration

Volume 2016 (2016), Article ID 8538165, 6 pages

http://dx.doi.org/10.1155/2016/8538165

## A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

^{1}Department of Computer, Charmo University, Sulaymaniyah, Iraq^{2}Department of Software Engineering, Koya University, Erbil, Iraq^{3}Halabja Institution, Halabja, Iraq

Received 8 November 2015; Revised 12 January 2016; Accepted 17 January 2016

Academic Editor: Arturo Garcia-Perez

Copyright © 2016 Zrar Kh. Abdul et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Gear fault detection is one of the underlying research areas in the field of condition monitoring of rotating machines. Many methods have been proposed as an approach. One of the major tasks to obtain the best fault detection is to examine what type of feature(s) should be taken out to clarify/improve the situation. In this paper, a new method is used to extract features from the vibration signal, called 1D local binary pattern (1D LBP). Vibration signals of a rotating machine with normal, break, and crack gears are processed for feature extraction. The extracted features from the original signals are utilized as inputs to a classifier based on -Nearest Neighbour (-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. The experiment results show that the 1D LBP method can extract the effective and relevant features for detecting fault in the gear. Moreover, we have adopted the LOSO and LOLO cross-validation approaches to investigate the effects of speed and load in fault detection.

#### 1. Introduction

It is a big challenge in fault detection and diagnostics to ensure the safe running of rotating machines. Vibration signal analysis has been widely used for fault diagnostics. With increasing improvements in vibration signal analysis, more accurate fault-detection techniques are being developed. In the area of gear fault detection, researchers are constantly investigating techniques for relevant features of fault detection.

Among several signal analysis methods, fast Fourier transform (FFT) is one of the most widely used and well-established methods. For instance, Pan and Sas in [1] conducted two tests, one to measure transient vibration signals and another to analyse the nonstationary vibration response of a rotor-dynamic system with both clutch and brake. Unfortunately, FFT-based methods are not suitable for nonstationary signal analysis and are not able to reveal the inherent information of nonstationary signals [1]. On the other hand, both wavelet scalogram and wavelet transform are effective methods for extracting relevant features of vibration signal for fault diagnostics of rotating machinery and are suitable for nonstationary signal analysis. In [2], statistical feature vectors were obtained using Morlet wavelet coefficients, which were utilized as the input into Support Vector Machine (SVM) classifiers. Al-Atat et al. in [3] developed a model that built specific fault signatures more visibly by applying wavelet decomposition into the row signal. However, the wavelet scalogram is incapable of achieving good time and spectral concentration in both the time and frequency space. Moreover, the wavelet transform cannot fully estimate the “good” features, because the vibration signal generates the structure of components, which makes it difficult to identify features for each component by wavelet transform alone [2, 4, 5]. Momoh and Dias [6] applied both FFT and wavelet transform to the extraction of features for fault detection and found that the wavelet transform scheme outperformed the FFT scheme.

Another method of fault detection is called Time Synchronous Average (TSA). TSA is a signal processing technique, which is used to extract repetitive signals from additive noise [7, 8]. Peng et al. [4] used a TSA technique in time and frequency domain. A TSA signal was obtained by applying the TSA technique to the vibration signal. Statistical features were then obtained from the TSA signal. Their results showed that the TSA in the frequency domain is more sensitive to fault detection; however, the spectral analysis may be incapable of detecting gear failures at an early stage [7]. Moreover, the TSA in frequency domain can be a successful technique if the frequency deterministic component is constant, but in reality a vibration signal contains small frequency variations [9, 10].

Do and Chong in [11] reported that the one-dimensional vibration signal could be converted to two-dimensional grayscale image. They extracted local features from the grayscale image and utilized scale invariant feature transform (SIFT). SIFT produced a 128-dimensional key point utilized for the classification of motor faults. The proposed method was efficient at diagnosing motor faults in the presence of background noise. However, there are some serious disadvantages of using SIFT. Firstly, there is an uncertainty in the number of key points for different images. Secondly, using SIFT has a high computational cost in processing 128-dimensional feature descriptors.

Shahriar et al. in [12] extracted an LBP feature from the images obtained from the vibration signal in order to create a fault diagnosis system for induction motors. These feature descriptors are then utilized by the classifier to diagnose faults on the motor. The method was effective in discriminating a normal and single fault in a time but was incapable of discriminating texture patterns for different fault categories. Moreover, the method required more complex computation such as the conversion of vibration signal into image followed by applying LBP.

In this paper, we use one-dimensional LBP inspired by the works in [13, 14], who were the first to adopt 1D LBP extraction from a one-dimensional speech signal. The advantage of 1D LBP is the possibility of choosing fewer than eight bits and consequently a smaller number of features. Additionally, there is no need to normalize the vibration signal value to be suitable to represent a proper image format. Our experimental results show comparable performance accuracy between our 1D LBP-based model that considers six neighbours and a 2D LBP scheme that exploits eight neighbours.

In order to investigate the effect of different conditions (speed and load), we adopt a special technique of cross-validation called Leave One Speed Out (LOSO) and Leave One Load Out (LOLO). This kind of cross-validation provides an experimental environment such that all the samples belonging to one condition will be used to test the model, while the model trained by samples belongs to different conditions.

Section 2 explains the processing of 1D LPB, Section 3 provides illustrations of data resulting from the experiments discussed in this paper, Section 4 explains the experiential work, Section 5 discusses the results obtained, and Section 6 reaches a conclusion.

#### 2. 1D Local Binary Pattern

The local binary pattern is a nonparametric operator. The LBP code can explain the data using the differences between a sample and its neighbours [15, 16]. LBPs have been widely used, particularly in face recognition systems [16–18]. At a fixed pixel position, the LBP operator is described as an ordered set of binary comparisons of pixel intensities between the centre pixel and its neighbouring pixels. However, LBPs used for images utilize the pixel neighbour in two dimensions, which is called 2D LBP.

Although it is not widely used, 1D LBP can provide similar characteristics to the 2D LBP. For example, the researchers in [13] showed a distinctive marker of certain features of the speech signal, where the 1D LBP features were able to distinguish the unvoiced and the voiced components of speech signals. Additionally, the authors of [14] adopted 1D LBP to segment and separate Voice Active Detection (VAD) of the speech signal.

The 1D LBP operator labels every single value of the vibration signal by considering its neighbourhoods and using the value of the centre position as a threshold for the neighbourhoods. If the neighbour value is less than the centre value, the value of the neighbour will turn to 0; otherwise it turns to 1. A local binary pattern code for a neighbourhood is then produced. The decimal value of the LBP binary code presents the local structural knowledge around the fixed value [15].

The histogram of the 1D LBP signal displays how often these various patterns appear in a given signal. The distribution of the patterns denotes the whole structure of the signal. The 1D LBP operation of a sample value can be defined aswhere the Sign function iswhere is the signal and is the number of considered neighbours. The Sign function transforms the differences to a -bit binary code.

In this paper only six neighbours are considered (three to the left of the centre and three to the right). Equation (1) illustrates how the 1D LBP is evaluated. Hence, the value range of the new signal is between 0 and 63. The obtained signal is discriminated into two parts, uniform and nonuniform number. The uniform number comprises the numbers with fewer than or equal to two transition bits from 1 to 0 or 0 to 1 in their circular bit patterns. The nonuniform numbers have more than two transition bits. For instance, the patterns 111111 (0 transitions) and 100011 (2 transitions) are uniform, while the patterns 10101 (4 transitions) and 010101 (6 transitions) are nonuniform. There are 21 uniform numbers in the range 0–63 and the rest are nonuniform numbers. The histogram is computed such that an independent bin represents each uniform number, while all the nonuniform numbers are represented in one bin. Therefore, the set of features consists of 22 bins—21 bins for each uniform number and one bin for all nonuniform numbers. These bins are utilized as features to detect fault. The number of bins in the histogram depends on how many neighbours are considered.

Figure 1 demonstrates a 1D LBP operator for with the centre sample as given. After processing 1D LBP, the 6-neighbour samples in the example above produce the 100101 codes. The code is then converted to a decimal number that is equal to 37 and substituted in the same index as the centre sample.