Monitoring, diagnostic and prognostic methods - II
Chair: Prof. Ronald BARRO (Mokpo National Maritime University)
Health Monitoring and Vibratory Fault Prediction of Rotating Machinery Prof. Qingkai HAN (Dalian University of Technology)
The major rotating machines such as large centrifugal or axial flow compressor, gas turbine and aero-engine are in the value chains of high-end and the core aspects of the industry factories, regarded as important embodiments of the national core competence in industry and high-technology development. The health monitoring and fault diagnosis and prediction, belonging to the technologies of prognosis and health management (PHM) are widely focused in recent years and developing constantly. The principles of health monitoring and vibratory fault prediction of rotating machinery are introduced in this paper. The dynamics of rotor system and structures are introduced, and the vibration problems of the rotating machine or structures are interpreted. The diagnosis and prediction of vibration faults happening on these machines commonly are given with examples of bearing faults of a turbine test-rig. At last, some important research tasks in future are prompted.
Bearing Fault Diagnosis Using Singular Spectrum Analysis-based Envelope Detection Dr. Guicai ZHANG (United Technologies Research Center (China) Ltd)
Rolling element bearings are critical components in rotating machines and it is important to monitor their health and detect their faults in early stage. The vibration energy generated by the faults in rolling element bearings is usually small comparing to that of other rotating parts such as rotors/shafts and gears in mechanical systems. Envelope analysis is a widely used method in bearing fault detection. The resonance frequency band identification is the key for narrow-band filtering before applying envelope operation. In the past over twenty years, researchers put forwarded some methods for identifying the resonance frequency band, such as Kurtogram, wavelet packets decomposition, etc. These methods are basically based on spectral kurtosis, i.e., using kurtosis as the criteria to determine the resonance frequency band. In some of the cases, the variance of the identified frequency band is too small and the results of the envelope are meaningless and ineffective. In this paper, a method based on singular spectrum analysis (SSA) and envelope analysis is proposed. The SSA is utilized to decompose the bearing vibration signal into a set of principle components (eigenvectors), and then a subset of the eigenvectors that encompass the dominant variation in the original signal is used for signal reconstruction. Envelope analysis is then applied to the reconstructed signal to extract the weak modulation information that caused by the bearing faults. The proposed SSA-based envelope analysis is applied to CWRU bearing data sets, and the results are compared with that of the widely used Kurtogram method. The results show that the SSA-based envelope analysis is effective for detection of the frequently occurred bearing faults and its detection rate is higher than other methods such as envelope detection based on Kurtogram and wavelet packets decomposition.
Development of an Effective Strategy for Prognostic Monitoring of a Large Centrifugal Air Compressor in an Automotive Plant Dr. Hyunsu KIM, Jay H KIM and Won Joon SONG (Ensemble Center for Automotive Research, University of Cincinnati, Dongshin University)
Prognostic monitoring of health condition of a large centrifugal air compressor that supplies compressed air in an automotive plant is crucial because its failure will seriously impair operation of the entire plant. It was desired to develop an effective prognostic maintenance methodology of air compressors after the failure of an air compressor in one of major automotive companies in US, which brought a highly undesirable situation to the manufacturing line of the plant. In this work, the shaft motion of the compressor measured at transient and steady-state conditions were used to develop techniques and a strategy for effective prognostic monitoring. The pseudo frequency response function (FRF) obtained from the Campbell diagram and directional Power Spectrum (dPS) were new techniques employed to develop the prognostic health monitoring strategy. The analytic wavelet transform (AWT) is adopted to monitor temporal change of the system characteristics during the start-up period. In addition, AWT was utilized to monitor the steady state condition.