Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters

Kaya, Yılmaz; Kuncan, Melih; Kaplan, Kaplan; Mi̇naz, Mehmet Recep; Ertunç, H. Metin

doi:10.1007/s00500-019-04656-2

Cited by 37 publications

(19 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different graph modeling-based approaches are also frequently proposed and implemented. Some worthy mentions are [7][8][9]20,[37][38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

show abstract

“…Different graph modeling-based approaches are also frequently proposed and implemented. Some worthy mentions are [7][8][9]20,[37][38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The experimental setup also consists of the signal amplifier, vibration sensors, data acquisition card, radial and axial load mechanism, and computer. Robust and artificial faulty bearings can be collected from this setup [23]- [25]. To create artificial defects, the bearing cages are divided into inner rings, outer rings, cages, and balls, as shown in Figure 2.…”

Section: Experimental Setup and Datasetmentioning

confidence: 99%

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Kuncan

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Bearings are vital automation machine elements that are used quite frequently for power transmission and shaft bearing in rotating machines. The healthy operation of the bearings directly affects the performance of the rotating machines. Bearing faults may cause more vibration than normal in rotating machines, which wastes power. However, further bearing failures can cause vital damage to rotating machines. In this study, bearing vibration values are obtained through a special test setup. Different types and different sizes of artificial faults have been created in the bearings for the testing process. Data on these bearings are collected at different speeds. The purpose of the study is to diagnose faults in the bearings. In this context, a new approach is proposed. First, the one-dimensional local binary pattern (1D-LBP) method is applied to vibration signals, and all signal data are carried to the 1D-LBP plane. Statistical features are obtained from the signals in the 1D-LBP plane by using these features, and then the vibrational signals are classified by the gray relational analysis (GRA) model. Four different data sets are organized to test the proposed approach. The results of the test process with this proposed model have an accuracy of 99.044% for Dataset1 (different speed − 300 rpm intervals), 94.224% for Dataset2 (different speed −60 rpm intervals), and 99.584% for Dataset3 (fault size (mm)); a 100% average success rate is observed for Dataset4 (fault type -error free bearing (EFB), inner ring fault (IRF), outer ring fault (ORF), and ball fault (BF)).

show abstract

“…Yılmaz Kaya et al [23] proposed a novel feature extraction method based on co-occurrence matrices for bearing vibration signals. The aforementioned Melih Kuncan, Yılmaz Kaya et al have been committed to applying the modified texture analysis to bearing fault diagnosis in recent years, and many enlightening results have been proposed [24]- [26]. The texture analysis or its variants are simple and effective, which provide stable features for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Pei

2021

Measurement Science Review

View full text Add to dashboard Cite

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

show abstract

Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters

Cited by 37 publications

References 24 publications

CNN-Based Fault Detection for Smart Manufacturing

CNN-Based Fault Detection for Smart Manufacturing

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Contact Info

Product

Resources

About