Author: Ching LiPublisher:
ISBN:
Category :
Languages : en
Pages : 153
Book Description
On-line Bearing Condition Monitoring by Pattern Recognition and Matched Filtering
Author: Ching LiOn-line Bearing Condition Monitoring by Pattern Recognition and Matched Filtering
Author: Jing LiProceedings of the International Conference on Emerging Technologies in Intelligent System and Control
Author: Publisher: Allied Publishers
ISBN: 9788177647297
Category : Artificial intelligence
Languages : en
Pages : 432
Book Description
Contributed articles presented in the seminar held during Jan. 5-7, 2005, at Kumaraguru College of Technology, Coimbatore.
On-line Monitoring of Milling Cutting Tool Condition
Author: Pau-Lo HsuThe Shock and Vibration Digest
Author: Proceedings
Author: Vibration Institute (Clarendon Hills, Ill.). MeetingPublisher:
ISBN:
Category : Machinery
Languages : en
Pages : 214
Book Description
Transactions of the North American Manufacturing Research Institution of SME.
Author: Publisher:
ISBN:
Category : Industrial engineering
Languages : en
Pages : 408
Book Description
On-line Identification of Machining Processes by Means of Instrumented Tools
Author: Markus Peter StrickerPublisher:
ISBN:
Category :
Languages : en
Pages : 416
Book Description
Non-parametric and Non-filtering Methods for Rolling Element Bearing Condition Monitoring
Author: Hamid FaghidiPublisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages : 0
Book Description
Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations. Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components. Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions. To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed: 1. An amplitude demodulation differentiation (ADD) method, 2. A calculus enhanced energy operator (CEEO) method, 3. A higher order analytic energy operator (HO_AEO) approach, and 4. A higher order energy operator fusion (HOEO_F) technique. The proposed methods have been evaluated using both simulated and experimental data.
Author: Christian Karrer