Multidimensional condition monitoring of machines in non-stationary operation

Cempel, C.; Tabaszewski, Maciej

doi:10.1016/j.ymssp.2006.04.001

Cited by 60 publications

(50 citation statements)

References 2 publications

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“…However, in some cases (wind turbines, mining machines, ships, helicopters etc. ), the machines work under non-stationary operating conditions (load and speed variation), that often require specific signal processing and pattern recognition suitable for time varying systems [1][2][3][4][5][6][7][8][9]. The wind turbine is a great example of such class of machines [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox

Zimroz¹,

Urbanek²,

Barszcz³

et al. 2011

Metrology and Measurement Systems

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Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

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Section: Introductionmentioning

confidence: 99%

Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox

Zimroz¹,

Urbanek²,

Barszcz³

et al. 2011

Metrology and Measurement Systems

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“…But the meaning of that relation with Σσ i (θ) seems not to be fully validated experimentally. Generally, it seems that the condition inference based on the above summation measures Σ(SD i ) may stand for the first approach to multidimensional condition inference, as was clearly shown in previous papers (Cempel et al, 2006a;Cempel et al, 2006b;Cempel, 2005;Żółtowski et al, 2004).…”

Section: Multidimensional Observation Of Conditions and The Extractiomentioning

confidence: 72%

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

Cempel¹

2008

International Journal of Applied Mathematics and Computer Science

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With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.

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“…(2). a i = n i+1 − n i /2 ( 2 ) MA approach is also applied to construct the local envelope function a 11 (t) in the same way as in step (1).…”

Section: Review Of Lmd Methodsmentioning

confidence: 99%

“…Because of the direct relationship between the vibration and the structure of the rotating machine, the vibrationbased signal processing techniques are widely used in the diagnostic field and have been proved to be effective in fault diagnosis of gearbox and roller bearing [2]. There are many signal processing techniques that can extract the fault information from the response signal such as time-domain features, envelope spectrum, wallet transform, demodulation analysis and so on [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A new rotating machinery fault diagnosis method based on improved local mean decomposition

Zhao

et al. 2015

Digital Signal Processing

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Multidimensional condition monitoring of machines in non-stationary operation

Cited by 60 publications

References 2 publications

Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox

Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

A new rotating machinery fault diagnosis method based on improved local mean decomposition

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