Download Condition Monitoring of Machinery in Non-Stationary by Robert B. Randall, Nader Sawalhi, Michael Coats (auth.), PDF

By Robert B. Randall, Nader Sawalhi, Michael Coats (auth.), Tahar Fakhfakh, Walter Bartelmus, Fakher Chaari, Radoslaw Zimroz, Mohamed Haddar (eds.)

ISBN-10: 3642287670

ISBN-13: 9783642287671

Condition tracking of machines in non-stationary operations (CMMNO) will be obvious because the significant problem for study within the box of equipment diagnostics.

Condition tracking of machines in non-stationary operations is the identify of the provided booklet and the name of the convention held in Hammamet - Tunisia March 26 – 28, 2012. it's the moment convention less than this identify, first came about in Wroclaw - Poland , March 2011.

The topic CMMNO comes at once from wishes and commentary of actual gadgets. so much monitored and clinically determined gadgets utilized in works in non-stationary operations situation. The non-stationary operations come from success of equipment initiatives, for which they're designed for. All equipment utilized in diversified type of mines, shipping platforms, cars like: autos, buses and so forth, helicopters, ships and battleships etc paintings in non-stationary operations.

The papers incorporated within the booklet are formed by way of the organizing board of the convention and authors of the papers.

The papers are divided into 5 sections, namely:

Condition tracking of machines in non-stationary operations

Modeling of dynamics and fault in systems

Signal processing and development recognition

Monitoring and diagnostic systems

Noise and vibration of machines

The provided publication supplies the again floor to the most target of the CMMNO 2012 convention that's to collect medical group to debate the most important advances within the box of equipment situation tracking in non-stationary stipulations.

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Read or Download Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Second International Conference "Condition Monitoring of Machinery in Non-Stationnary Operations" CMMNO’2012 PDF

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Extra resources for Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Second International Conference "Condition Monitoring of Machinery in Non-Stationnary Operations" CMMNO’2012

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The wavelet coefficients in the set of terminal nodes contain all information regarding the analysed signal. The analysis of the envelope A(ω ,t) was performed by analysing the signal’s energy within each terminal node. Each terminal node (d, n) contains Nd wavelet coefficients Pdn (k), where k = 0, . . , Nd − 1 and the number Nd depends on the length of the signal and the depth d of the WPT tree. Using these coeficients, the portion of the signal’s energy Ed,n contained within one node (d, n) reads [6]: Ed,n = Nd −1 ∑ 2 Pdn (k) (5) k=0 f(t) (1,1) (1,0) (2,0) (3,0) (2,2) (2,1) (3,1) (3,2) (3,3) (3,4) (2,3) (3,5) (3,6) (3,7) Fig.

8b (when object is in bad condition) but with increase of the load the value of the fault increase linearly. 10. The use of susceptibility characteristics and its interpretation comes from the result of a study on influence: design, production technology, operation and change of condition factors. Further examples of using the susceptibility characteristics are given in the cited papers and will be also given during the paper presentation. Acknowledgment. This paper was financially supported by Polish State Committee for Scientific Research 2010-2013 as research project NN 504147838.

Conversely, the problems of estimation of the remaining useful life of mechanical drives under variable operating conditions are left largely neglected. There are a 26 P. Boškoski and Ð. Juriˇci´c few examples addressing the problems of prognostics under such conditions [9, 7]. Although the proposed approaches for condition monitoring and prognostics and health management give satisfactory results they heavily depend on accurate measurements of the current operating conditions. For the purpose of prognostics, in this paper we propose an approach based on the analysis of the statistical complexity of signals [16, 20], a concept readily applied in the analysis of EEG signals [14].

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