Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault

Design of health condition monitoring system of mine hoisting equipment

In view of problem that health condition monitoring system of existing mine hoist equipment uses a single signal for condition monitoring which easily leads to misjudgment, health condition monitoring system of mine hoisting equipment based on signal fusion was designed. The system collects spindle torque signal, bearing vibration signal and wire rope tension signal during operation of mine hoisting equipment respectively. The signal fusion method of decision level was used to calculate and fuse in host computer, and fusion judgment result was displayed. The test results show that the system can effectively monitor health condition of mine hoisting equipment

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings

Rural Residents in China Are at Increased Risk of Exposure to Tick-Borne Pathogens Anaplasma phagocytophilum and Ehrlichia chaffeensis

As emerging tick born rickettsial diseases caused by A. phagocytophilum and E. chaffeensis, anaplasmosis and ehrlichiosis have become a serious threat to human and animal health throughout the world. In particular, in China, an unusual transmission of nosocomial cases of human granulocytic anaplasmosis occurred in Anhui Province in 2006 and more recent coinfection case of A. phagocytophilum and E. chaffeensis was documented in Shandong Province. Although the seroprevalence of human granulocytic anaplasmosis (former human granulocytic ehrlichiosis, HGE) has been documented in several studies, these data existed on local investigations, and also little data was reported on the seroprevalence of human monocytic ehrlichiosis (HME) in China. In this cross-sectional epidemiological study, indirect immunofluorescence antibody assay (IFA) proposed by WHO was used to detect A. phagocytophilum and E. chaffeensis IgG antibodies for 7,322 serum samples from agrarian residents from 9 provinces/cities and 819 urban residents from 2 provinces. Our data showed that farmers were at substantially increased risk of exposure. However, even among urban residents, risk was considerable. Seroprevalence of HGA and HME occurred in diverse regions of the country and tended to be the highest in young adults. Many species of ticks were confirmed carrying A. phagocytophilum organisms in China while several kinds of domestic animals including dog, goats, sheep, cattle, horse, wild rabbit, and some small wild rodents were proposed to be the reservoir hosts of A. phagocytophilum. The broad distribution of vector and hosts of the A. phagocytophilum and E. chaffeensis, especially the relationship between the generalized susceptibility of vectors and reservoirs and the severity of the disease’s clinical manifestations and the genetic variation of Chinese HGA isolates in China, is urgently needed to be further investigated