scholarly journals Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions

2020 ◽  
Author(s):  
Alexandre Mauricio ◽  
Dustin Helm ◽  
Markus Timusk ◽  
Jerome Antoni ◽  
Konstantinos Gryllias
Keyword(s):  
Condition Monitoring ◽  
Gas Turbines ◽  
Business Models ◽  
Industrial Process ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Cyclic Behavior ◽  
Spectral Correlation ◽  
Time Frequency ◽  
Novel Approach

Abstract Condition monitoring arises as a valuable industrial process in order to assess the health of rotating machinery, providing early and accurate warning of potential failures and allowing for the planning and effective realization of preventative maintenance actions. Nowadays machinery (gas turbines, wind turbines etc.) manufacturers adopt new business models, providing not only the equipment itself but additionally taking on responsibilities of condition monitoring, by embedding sensors and health monitoring systems within each unit and prompting maintenance actions when necessary. Among others, rolling element bearings are one of the most critical components in rotating machinery. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations (e.g. gears, shafts, rotors etc.). Vibration analysis is most widely used and various methods have been proposed, including analysis in the time and frequency domain. In a number of applications, changes in the operating conditions (speed/load) influence the vibration sources and change the frequency and amplitude characteristics of the vibroacoustic signature, making them nonstationary. Under changing environments, where speed and load vary, the assumption of quasi-stationary is not appropriate and as a result a number of time-frequency and time-order representations have been introduced, such as the Short Time Fourier Transform and the Wavelets. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. The classical cyclostationary tools, such as the Cyclic Spectral Correlation Density (CSCD) and the Cyclic Modulation Spectrum (CMS), can be used in order to extract interesting information about the cyclic behavior of cyclostationary signals, only under the assumption that the speed of machinery is constant or nearly constant. Global diagnostic indicators have been proposed as a measure of cyclostationarity under steady operating conditions. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency as well as diagnostic indicators of cyclo-non-stationarity in order to cover the speed varying operating conditions. The scope of this paper is to propose a novel approach for the analysis of cyclo-non-stationary signals based on the generalization of indicators of cyclo-non-stationarity in order to cover the simultaneous and independently varying speed and load operating conditions. The effectiveness of the approach is evaluated on simulated and real signals captured on a dedicated test rig.

scholarly journals Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions

2021 ◽  
Author(s):  
Alexandre Mauricio ◽  
Dustin M Helm ◽  
Markus Timusk ◽  
Jerome Antoni ◽  
Konstantinos Gryllias
Keyword(s):  
Industrial Process ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Cyclic Behavior ◽  
Spectral Correlation ◽  
Rotating Speed ◽  
Modulation Spectrum ◽  
Novel Approach ◽  
Cyclostationary Signals ◽  
Bearing Damage

Abstract Condition monitoring arises as a valuable industrial process in order to assess the health of rotating machinery, providing early and accurate warning of potential failures and allowing for the planning and effective realization of preventative maintenance actions. In complex machines the failure indications of an early bearing damage are weak compared to other sources of excitations. Vibration analysis is most widely used and various methods have been proposed. In a number of applications, changes in the operating conditions (speed/load) influence the vibration sources and change the frequency and amplitude characteristics of the vibroacoustic signature, making them nonstationary. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary. Classical cyclostationary tools, such as Cyclic Spectral Correlation Density (CSCD) and Cyclic Modulation Spectrum (CMS), can be used to extract information about the cyclic behavior of cyclostationary signals, under the assumption of nearly constant rotating speed. Global diagnostic indicators have been proposed as a measure of cyclostationarity under steady operating conditions. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency as well as diagnostic indicators of cyclo-non-stationarity in order to cover the speed varying operating conditions. The scope of this paper is to propose a novel approach for the analysis of cyclononstationary signals to cover the simultaneous and independently varying speed and load operating conditions. The effectiveness of the approach is evaluated on simulated and real signals captured on a dedicated test rig.

scholarly journals Application of Cyclo-Non-Stationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Computational Cost ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Cyclic Behavior ◽  
Diagnostic Features ◽  
Time Frequency

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.

scholarly journals Application of Cyclo-Nonstationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Aircraft Engine ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Diagnostic Features ◽  
Time Frequency ◽  
Acceleration Signal

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health-monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring, and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time–frequency domain, and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclo-stationary, which is a particular class of nonstationary stochastic processes. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclo-stationarity (ICNS) in order to cover the speed-varying conditions. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN.

scholarly journals Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

2020 ◽  
Vol 10 (2) ◽  
pp. 506 ◽  
Author(s):  
Emmanuel Resendiz-Ochoa ◽  
Juan J. Saucedo-Dorantes ◽  
Juan P. Benitez-Rangel ◽  
Roque A. Osornio-Rios ◽  
Luis A. Morales-Hernandez
Keyword(s):  
Supervised Learning ◽  
Infrared Thermography ◽  
Condition Monitoring ◽  
Infrared Imaging ◽  
Laboratory Data ◽  
Industrial Process ◽  
Operating Conditions ◽  
Data Set ◽  
Linear Discriminant

In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data-Based Study

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Bin Zhou
Keyword(s):  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Evaluation Process ◽  
Failure Detection ◽  
Operating Conditions ◽  
Steam Turbines ◽  
Loss Event ◽  
A Chain

In situ condition monitoring (CM) is a crucial element in protection and predictive maintenance of large rotating PowerGen equipment, such as gas turbines or steam turbines. In this work, selected gas turbine loss events occurring during a recent 10-year period at our clients’ power generation plants were evaluated. For each loss event, a loss scenario or a chain of failures was outlined after investigating the available loss record. These loss events were then categorized based on the nature of the associated loss scenario. The study subsequently focused on the variables that could be monitored in real-time to detect the abnormal turbine operating conditions, such as vibration characteristics, temperature, pressure, quality of working fluids, and material degradations. These groups of CM variables were then matched with detectable failures in each loss event and prioritized based on their effectiveness for failure detection and prevention. The detectable loss events and the associated loss values were used in this evaluation process. The study finally concluded with a summary of findings and path-forward actions.

scholarly journals Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8468
Author(s):  
Martí de Castro-Cros ◽  
Manel Velasco ◽  
Cecilio Angulo
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Industrial Process ◽  
Industrial Sector ◽  
Key Factors ◽  
Challenges And Opportunities ◽  
Turbine Equipment ◽  
Industrial Gas Turbine ◽  
Available Information

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery

2013 ◽  
Vol 588 ◽  
pp. 214-222 ◽  
Author(s):  
Ryszard Makowski ◽  
Radoslaw Zimroz
Keyword(s):  
Damage Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Time Varying ◽  
Local Damage ◽  
Time Frequency ◽  
Vibration Data ◽  
Novel Approach ◽  
Vibration Signal Analysis

The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

Application of Virtual Instrument on Condition Monitoring and Fault Diagnosis System of the Rotating Machinery

2012 ◽  
Vol 542-543 ◽  
pp. 161-164
Author(s):  
Yong Ying Du ◽  
Yu Ning Wang ◽  
Ming Ang Yin
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Rotating Machinery ◽  
Waveform Data ◽  
Time Frequency ◽  
Acceleration Sensors

In the paper it can be easier to realize the acquisition of the rotating machinery vibration signal and condition monitoring through the configuration the platform of virtual instrumentation. For the data acquisition it is enough to be plus with two acceleration sensors and a counter. The system is divided into parameter setting module, data acquisition, storage and display module, amplitude domain analysis module, time-domain analysis module, frequency domain analysis module, time-frequency domain analysis module and fault diagnosis module. The signal acquisition is got by using the PCI-6024E data acquisition card. And it is can be saved as binary data stream files and waveform data file according to the requirements of the sequence data processing. Signal analysis is conducted by using LabVIEW software and draw out the vibration spectrum diagram in order to achieve fault diagnosis of rotating machinery.

PowerGen Gas Turbine Losses and Condition Monitoring: A Loss Data Based Study

2014 ◽  
Author(s):  
Bin Zhou
Keyword(s):  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Evaluation Process ◽  
Failure Detection ◽  
Operating Conditions ◽  
Steam Turbines ◽  
Loss Event ◽  
A Chain

In-situ condition monitoring (CM) is a crucial element in protection and predictive maintenance of large rotating Power-Gen equipment such as gas turbines or steam turbines. In this work, selected gas turbine loss events occurring during a recent ten-year period at FM Global clients’ power generation plants were evaluated. For each loss event, a loss scenario or a chain of failures was outlined after investigating the available loss record. These loss events were then categorized based on the nature of the associated loss scenario. The study subsequently focused on the variables that could be monitored in real time to detect the abnormal turbine operating conditions, such as vibration characteristics, temperature, pressure, quality of working fluids and material degradations. These groups of condition monitoring variables were then matched with detectable failures in each loss event and prioritized based on their effectiveness for failure detection and prevention. The detectable loss events and the associated loss value were used in this evaluation process. The study finally concluded with a summary of findings and path-forward actions.

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