Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump

Abstract The core of a helicopter drivetrain is a complex planetary main gearbox (MGB) which reduces the high input speed generated by the engines in order to provide the appropriate torque to the main rotors and to other auxiliary systems. The gearbox consists of various shafts, planetary gears and bearings and operates under varying conditions under excessive friction, heat and high mechanical forces. The components are vulnerable to fatigue defects and therefore Health and Usage Monitoring Systems (HUMS) have been developed in order to monitor the health condition of the gearbox, focusing towards early, accurate and on time fault detection with limited false alarms and missed detections. The main aim of a HUM System is by health monitoring to enhance the helicopters’ operational reliability, to support the maintenance decision making, and to reduce the overall maintenance costs. The importance and the need for more advanced and accurate HUMS have been emphasized recently by the post-accident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. During the last few decades various methodologies and diagnostic indicators/features have been proposed for the monitoring of rotating machinery operating under steady conditions but still there is no global solution for complex structures. A new tool called IESFOgram has been recently proposed by the authors, based on Cyclostationary Analysis, focusing on the accurate selection of a filtering band, under steady and varying speed conditions. Moreover the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper the performance of the tool is tested on a complex planetary gearbox, with several vibration sources. The method is tested, evaluated and compared to state of the art methods on a dataset captured during experimental tests under various operating conditions on a Category A Super Puma SA330 main planetary gearbox, presenting seeded bearing defects of different sizes.

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A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK

Journal of Sensors ◽

10.1155/2019/6207847 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Yu Zhang ◽

Zhuoyou Fan ◽

Xiaorong Gao ◽

Lin Luo

Keyword(s):

Fault Location ◽

Simulation Analysis ◽

Spline Interpolation ◽

Microphone Array ◽

Computing Time ◽

Rolling Bearing ◽

Distortion Correction ◽

Bearing Defects ◽

Envelope Spectrum ◽

The Impact

Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.

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Vibration-Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4044453 ◽

2020 ◽

Vol 142 (3) ◽

Author(s):

Alexandre Mauricio ◽

Linghao Zhou ◽

David Mba ◽

Konstantinos Gryllias

Keyword(s):

Experimental Tests ◽

Operational Reliability ◽

Health Condition ◽

Operating Conditions ◽

False Alarms ◽

Planetary Gearbox ◽

Bearing Defects ◽

Feature Optimization ◽

Envelope Spectrum ◽

Selection Of

Abstract The core of a helicopter drivetrain is a complex planetary main gearbox (MGB), which reduces the high input speed generated by the engines in order to provide the appropriate torque to the main rotors and to other auxiliary systems. The gearbox consists of various shafts, planetary gears, and bearings, and operates under varying conditions under excessive friction, heat, and high mechanical forces. The components are vulnerable to fatigue defects and therefore health and usage monitoring systems (HUMS) have been developed in order to monitor the health condition of the gearbox, focusing toward early, accurate, and on-time fault detection with limited false alarms and missed detections. The main aim of a HUMS is by health monitoring to enhance the helicopters' operational reliability, to support the maintenance decision-making, and to reduce the overall maintenance costs. The importance and the need for more advanced and accurate HUMS have been emphasized recently by the postaccident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. During the last few decades, various methodologies and diagnostic indicators/features have been proposed for the monitoring of rotating machinery operating under steady conditions but still there is no global solution for complex structures. A new tool called improved envelope spectrum via feature optimization-gram (IESFOgram) has been recently proposed by the authors, based on cyclostationary analysis, focusing on the accurate selection of a filtering band, under steady and varying speed conditions. Moreover, the cyclic spectral coherence (CSCoh) is integrated along the selected frequency band leading to an improved envelope spectrum (IES). In this paper, the performance of the tool is tested on a complex planetary gearbox, with several vibration sources. The method is tested, evaluated, and compared to state-of-the-art methods on a dataset captured during experimental tests under various operating conditions on a Category A Super Puma SA330 main planetary gearbox, presenting seeded bearing defects of different sizes.

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Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection

MATEC Web of Conferences ◽

10.1051/matecconf/202032703003 ◽

2020 ◽

Vol 327 ◽

pp. 03003

Author(s):

Hui Li ◽

Xuhan Liu

Keyword(s):

Empirical Mode Decomposition ◽

Vibration Signal ◽

Rolling Bearing ◽

Intrinsic Mode Functions ◽

Bearing Fault ◽

Spectral Kurtosis ◽

Bearing Defects ◽

Mode Decomposition ◽

Kurtosis Parameter ◽

Envelope Spectrum

A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.

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On corrosion properties of ceramic materials for pump friction pairs in lead – bismuth environment

Voprosy Materialovedeniya ◽

10.22349/1994-6716-2019-98-2-116-122 ◽

2019 ◽

pp. 116-122

Author(s):

V. V. Stepanov ◽

A. D. Kashtanov ◽

S. U. Shchutsky ◽

A. N. Agrinsky ◽

N. I. Simonov

Keyword(s):

Composite Materials ◽

Heat Resistance ◽

Experimental Research ◽

Ceramic Materials ◽

Operating Conditions ◽

Metallic Materials ◽

Corrosion Properties ◽

Liquid Coolant ◽

Radial Bearing ◽

Axial Pump

We consider the results of studies on the choice of material of the lower radial bearing of the pump, designed to circulate the coolant lead – bismuth. The circulation of the liquid coolant is provided by a vertical axial pump having a “long” shaft. In this design it is necessary to provide for the lower bearing the lubrication carried out with lead – bismuth coolant. Having analyzed the operating conditions of the axial pump, we decided to carry out the lower bearing in accordance with the scheme of a hydrodynamic sliding bearing. The materials of friction pairs in such a bearing must withstand the stresses arising from the operation of the pump, as well as the aggressive conditions of the coolant. Non-metallic materials – ceramics and carbon-based composite materials – were selected basing on the study of literature data for experimental research on the corrosion and heat resistance in the lead-bismuth environment.

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Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

Journal of Vibration and Acoustics ◽

10.1115/1.4043508 ◽

2019 ◽

Vol 141 (5) ◽

Author(s):

Wei Xiong ◽

Qingbo He ◽

Zhike Peng

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Feature Matching ◽

Matching Pursuit ◽

Microphone Array ◽

Time Frequency ◽

Moving Sources ◽

The Time Domain ◽

Envelope Spectrum ◽

Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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A new fault diagnosis method based on adaptive spectrum mode extraction

Structural Health Monitoring ◽

10.1177/1475921720986945 ◽

2021 ◽

pp. 147592172098694

Author(s):

Zhijian Wang ◽

Ningning Yang ◽

Naipeng Li ◽

Wenhua Du ◽

Junyuan Wang

Keyword(s):

Scale Space ◽

Variational Mode Decomposition ◽

Rolling Bearings ◽

Mode Decomposition ◽

Penalty Factor ◽

Diagnosis Method ◽

Mode Extraction ◽

Non Stationary Signal ◽

Envelope Spectrum ◽

Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

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Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽

10.3390/en14154514 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4514

Author(s):

Vincent Becker ◽

Thilo Schwamm ◽

Sven Urschel ◽

Jose Alfonso Antonino-Daviu

Keyword(s):

Condition Monitoring ◽

False Alarms ◽

Signature Analysis ◽

Variable Speed ◽

Monitoring Methods ◽

Time Frequency ◽

Motor Current ◽

Load Point ◽

Bearing Defects ◽

Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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