Rotor Blades Condition Monitoring Method Based on the Elimination of the Environment Signal

Lindstedt, Paweł; Rokicki, Edward; Borowczyk, Henryk; Majewski, Paweł

doi:10.2478/v10041-009-0005-y

Cited by 3 publications

(9 citation statements)

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“…In this situation, it seems reasonable to switch from the time domain "t" of the signals x(t) and y(t) to the time domain "τ" of correlation function R xx (τ),R yy (τ) and R xy (τ) [13,15,17,18,19,21].…”

Section: Theoretical Bases Of the Blade Diagnostics Methods During Turmentioning

confidence: 99%

“…Characteristic features of this model are: the possibility of determination only on the basis of the measurable signal y(t) in observation periods T 01 and T 12 that occur shortly one after another, and, what is the most important, the possibility of taking environment signal x(t) into account without the need to measure it, as well as sufficient noise suppression in signal y(t) [13,15,17,18].…”

Section: Theoretical Bases Of the Blade Diagnostics Methods During Turmentioning

confidence: 99%

“…Practice proved many times that there are real difficulties in the process of signals measurement, both for y(t), and x(t) (even to a greater extent), and thus also in the evaluation of blade technical condition during machine operation [13,15,17,18].…”

Section: Blade Operation In Stochastic Environmentmentioning

confidence: 99%

“…-blade fracture indicator: SPŁ -29 [17,18,23,24]; -blade excessive vibration indicator: SNDŁ -2b [17,18,23,24]; -microwave sensors: MUH, PIT [17,18,23,24]. These systems work successfully in particular operational technical objects (SO-3 engines).…”

Section: Introductionmentioning

confidence: 99%

“…Fig. 1 shows construction and functioning of a blade during its operation in variable environment [17,18,21].…”

Section: Introductionmentioning

confidence: 99%

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Model for Blade Diagnosis in a Working Rotor Machine Employing the Method of Virtual Elimination of Stochastic Environment

Lindstedt¹,

Grądzki²

2011

Archive of Mechanical Engineering

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This paper presents the bases of a new method of monitoring technical condition of turbomachine blades during their operation. The method utilizes diagnostic models such as a quotient of amplitude amplification and a phase shift of a diagnostic signal y(t) which is a result of blade operation as well as a signal x(t) of blade environment while a blade tip approaches a sensor, amplitude amplification and phase shift of these signals while the blade tip moves away from the sensor.The adopted diagnostic models indirectly take into account the existing environment of a blade, represented by the signal x(t), without the need to measure it [13,15]. Thus, the model is sensitive to the changes in technical condition of blades and practically insensitive to a change in environment. The suggested method may prove very important in diagnostics of rotor blades during turbomachines operation (compressors, turbines etc.).

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Section: Theoretical Bases Of the Blade Diagnostics Methods During Turmentioning

confidence: 99%

Section: Theoretical Bases Of the Blade Diagnostics Methods During Turmentioning

confidence: 99%

Section: Blade Operation In Stochastic Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Fig. 1 shows construction and functioning of a blade during its operation in variable environment [17,18,21].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model for Blade Diagnosis in a Working Rotor Machine Employing the Method of Virtual Elimination of Stochastic Environment

Lindstedt¹,

Grądzki²

2011

Archive of Mechanical Engineering

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Method of shaft crack detection based on squared gain of vibration amplitude

2019

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Rotating machines are exposed to different faults such as shaft cracks, bearing failures, rotor misalignment, stator to rotor rub, etc. Therefore, turbogenerators, aircraft engines, compressors, pumps, and many other rotating machines should be constantly diagnosed to warn about the probable appearance of a possible rotor failure. Unfortunately, despite the ongoing work on various rotor fault detection methods, there are still very few techniques that can be considered as reliable and applicable in practical problems. The difficulty lies in the fact that usually the fault introduces very subtle local changes in the overall structure of the rotor. The symptoms of these changes must be isolated and extracted from a wide spectrum of vibration data obtained from sensors measuring the vibrations of the machine. The measured data are usually disturbed with some noise or other disturbances, and that is why the detection of a possible rotor fault is even more difficult. The paper presents a new rotor fault detection method. The method is based on a new diagnostic model of rotor signals and external disturbances. The model utilizes auto-correlation functions of measured rotor's vibrations. By proper processing of the measured vibration data, the influence of environmental disturbances is completely compensated and reliable indications of the possible rotor fault are obtained. The method has been tested numerically using the finite element model of

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An expert system aided method of the blade vibration sources identification with blade-tip timing (BTT) signal decomposition

2018

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An expert system aided method of the blade-tip signal decomposition to the turbine blade vibration sources identification is presented. The method utilises a multi-valued diagnostic model based on the discrete wavelet transform. Proposed algorithm consists of four stages: signal decomposition into low- and high-frequency components (approximations and details), approximations and details parameterization, multi-valued encoding of parameters obtained at the second stage, an expert system use of the turbine blade vibration sources identification.

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Rotor Blades Condition Monitoring Method Based on the Elimination of the Environment Signal

Cited by 3 publications

References 0 publications

Model for Blade Diagnosis in a Working Rotor Machine Employing the Method of Virtual Elimination of Stochastic Environment

Model for Blade Diagnosis in a Working Rotor Machine Employing the Method of Virtual Elimination of Stochastic Environment

Method of shaft crack detection based on squared gain of vibration amplitude

An expert system aided method of the blade vibration sources identification with blade-tip timing (BTT) signal decomposition

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