Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-Naïve Bayes Classification

The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. It becomes essential to correctly diagnose the fault caused by the bearing. Hence the importance of determining an effective features extraction method that best describes the fault. The vision of this paper is to merge the features selection methods in order to define the most relevant featuresin the texture of the vibration signal images. In this study, the Gray Level Co-occurrence Matrix (GLCM) in texture analysis is applied on the vibration signal represented in images. Features selection based on the merge of PCA (Principal component Analysis) method and SFE (Sequential Features Extraction) method is done to obtain the most relevant features. The multiclass-Na?ve Bayesclassifier is used to test the proposed approach. The success rate of this classification is 98.27%. The relevant features obtained give promising results and are more efficient than the methods observed in the literature.

Feature Selection by Merging Sequential Bidirectional Search into Relevance Vector Machine in Condition Monitoring

For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique（feature selection methods） are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types： filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

Analysis of the Diagnostic Consistency of Chinese Medicine Specialists in Cardiovascular Disease Cases and Syndrome Identification Based on the Relevant Feature for Each Label Learning Method

Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.Methods：Using self-developed CM clinical scales to collect cases,inquiry information,complexity,tongue manifestation and pulse manifestation were assessed.The number of cases collected was 2,218.Firstly,each case was differentiated by two CM specialists according to the same diagnostic criteria.The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed.Secondly,take the same diagnosis syndromes of two specialists as the results of the cases.According to injury information in the CM scale ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂,and according to the syndrome type in each case ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂.CM information data on cardiovascular disease cases were established.We studied CM syndrome classification and identification based on the relevant feature for each label（REAL） leaming method,and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5,10,15,20,30,50,70,and 100,respectively.Results：The syndromes with good diagnostic consistency were Heart（Xin）-qi deficiency,Heart-yang deficiency,Heart-yin deficiency,phlegm,stagnation of blood and stagnation of qi.Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver（Gan）.The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency.A different number of features,such as 5,10,15,20,30,40,50,70,and 100,respectively,were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy.The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.Conclnsions：CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency.The REAL method fully considers the relationship between syndrome types and injury symptoms,and is suitable for the establishment of models for CM syndrome classification and identification.This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.