3 research outputs found
Prognostics of Ball Bearings in Cooling Fans
Ball bearings have been used to support rotating shafts in machines such as wind turbines, aircraft engines, and desktop computer fans. There has been extensive research in the areas of condition monitoring, diagnostics, and prognostics of ball bearings. As the identification of ball bearing defects by inspection interrupts the operation of rotating machines and can be costly, the assessment of the health of ball bearings relies on the use of condition monitoring techniques. Fault detection and life prediction methods have been developed to improve condition-based maintenance and product qualification. However, intermittent and catastrophic system failures due to bearing problems still occur resulting in loss of life and increase of maintenance and warranty costs. Inaccurate life prediction of ball bearings is of concern to industry. This research focuses on prognostics of ball bearings based on vibration and acoustic emission analysis to provide early warning of failure and predict life in advance. The failure mechanisms of ball bearings in cooling fans are identified and failure precursors associated with the defects are determined. A prognostic method based on Bayesian Monte Carlo method and sequential probability ratio test is developed to predict time-to-failure of ball bearings in advance. A benchmark study is presented to demonstrate the application of the developed prognostic method to desktop computer fans. The prognostic method developed in this research can be extended as a general method to predict life of a component or system
Diagnosis of low-speed bearing degradation using acoustic emission techniques
It is widely acknowledged that bearing failures are the primary reason for
breakdowns in rotating machinery. These failures are extremely costly,
particularly in terms of lost production. Roller bearings are widely used in
industrial machinery and need to be maintained in good condition to ensure the
continuing efficiency, effectiveness, and profitability of the production process.
The research presented here is an investigation of the use of acoustic emission
(AE) to monitor bearing conditions at low speeds.
Many machines, particularly large, expensive machines operate at speeds below
100 rpm, and such machines are important to the industry. However, the
overwhelming proportion of studies have investigated the use of AE techniques
for condition monitoring of higher-speed machines (typically several hundred
rpm, or even higher). Few researchers have investigated the application of these
techniques to low-speed machines (<100 rpm), This PhD addressed this
omission and has established which, of the available, AE techniques are suitable
for the detection of incipient faults and measurement of fault growth in low-speed
bearings.
The first objective of this research program was to assess the applicability of AE
techniques to monitor low-speed bearings. It was found that the measured
statistical parameters successfully monitored bearing conditions at low speeds
(10-100 rpm).
The second objective was to identify which commonly used statistical parameters
derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify
the onset of a fault in either race. It was found that the change in AE amplitude
and AE RMS could identify the presence of a small fault seeded into either the
inner or the outer races. However, the severe attenuation of the signal from the
inner race meant that, while AE amplitude and RMS could readily identify the
incipient fault, kurtosis and the AE counts could not. Thus, more attention needs
to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of
severity of the fault. However, once the defect was established, it was found that
of the parameters used only AE RMS was sensitive to defect size.
The fourth objective was to assess whether the AE signal is able to detect defects
located at either the centre or edge of the outer race of a bearing rotating at low
speeds. It is found that all the measured AE parameters had higher values when
the defect was seeded in the middle of the outer race, possibly due to the shorter
path traversed by the signal between source and sensor which gave a lower
attenuation than when the defect was on the edge of the outer race. Moreover,
AE can detect the defect at both locations, which confirmed the applicability of
the AE to monitor the defects at any location on the outer race
Failure Prediction of Digitally Controlled Switching Mode Power Supply
長崎大学学位論文 学位記番号:博(工)甲第59号 学位授与年月日:平成30年9月20日Nagasaki University (長崎大学)課程博