Using Triaxial Accelerometer Data for Vibration Monitoring of Helicopter Gearboxes

2001 ◽  
Author(s):  
Irem Y. Tumer ◽  
Edward M. Huff
Keyword(s):  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Accelerometer Data ◽  
Frequency Content ◽  
Additional Information ◽  
Vibration Data ◽  
Statistical Variation ◽  
Novel Method ◽  
Monitoring And Diagnostics ◽  
Vibration Signatures

Abstract Typical vibration monitoring systems for helicopter gearboxes rely on single-axis accelerometer data. This paper investigates whether triaxial accelerometers can provide crucial flight regime information for helicopter gearbox monitoring systems. The frequency content of the three different directions is compared and analyzed using time-synchronously averaged vibration data. The triaxial data are decorrelated using a mathematical transformation, and compared to the original axes to determine their optimality. The benefits of using triaxial data for vibration monitoring and diagnostics are explored by analyzing the changes in the direction of the principal axis of vibration formed using all three axes of vibration. The statistical variation introduced due to the experimental variables is further analyzed using an Analysis of Variance approach to determine the effect of each variable on the overall signature. The results indicate that triaxial accelerometers can provide additional information about the frequency content of helicopter gearbox vibrations, providing researchers and industry with a novel method of capturing and monitoring changes in the baseline vibration signatures.

Analysis of Triaxial Vibration Data for Health Monitoring of Helicopter Gearboxes

2003 ◽  
Vol 125 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Irem Y. Tumer ◽  
Edward M. Huff
Keyword(s):  
Vibration Monitoring ◽  
False Alarms ◽  
Frequency Content ◽  
Sources Of Information ◽  
Additional Information ◽  
Vibration Data ◽  
Statistical Variation ◽  
Monitoring And Diagnostics ◽  
Helicopter Transmission ◽  
Vibration Signatures

Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, are explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. The frequency content of the three different directions is compared and analyzed using time-synchronously averaged vibration data. To provide a method for analysis and monitoring purposes, the triaxial data are decorrelated using a mathematical transformation, and compared to the original axes to determine their differences. The benefits of using triaxial data for vibration monitoring and diagnostics are explored by analyzing the changes in the direction of the principal axis of vibration formed using all three axes of vibration. The statistical variation introduced due to the experimental variables is further analyzed using an Analysis of Variance approach to determine the effect of each variable on the overall signature. The results indicate that triaxial accelerometers can provide additional information about the frequency content of helicopter gearbox vibrations, and provide researchers and industry with a novel method of capturing and monitoring triaxial changes in the baseline vibration signatures.

Recent Developments in Vibration Monitoring and Diagnostics for Aeroderivative Gas Turbines

1985 ◽  
Author(s):  
James C. Adams
Keyword(s):  
Gas Turbines ◽  
High Efficiency ◽  
Industrial Applications ◽  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Monitoring Methods ◽  
Reliable Monitoring ◽  
Recent Developments ◽  
Monitoring And Diagnostics ◽  
Processing Techniques

Industrial aeroderivative gas turbines are becoming increasingly popular for use in both on-shore and off-shore installations. The characteristics of these machines — high efficiency in simple cycle operation, small size, and light weight — make them ideal for industrial applications. As the aeroderivative gas turbine has become more widely used, the need for more reliable monitoring methods has become increasingly apparent. Traditional velocity transducer based seismic monitoring systems have had several shortcomings when applied to aeroderivative gas turbines. One of these problems was nuisance alarms due to increasing transducer noise output. Another was not detecting increasing casing vibration because of transducer deterioration. Overcoming these problems has required advances in transducer technology as well as changes in signal processing techniques. This paper describes the technology and techniques used in new seismic vibration monitoring systems.

Periodic and Continuous Vibration Monitoring for Preventive/Predictive Maintenance of Rotating Machinery

1987 ◽  
Vol 109 (2) ◽  
pp. 159-167 ◽  
Author(s):  
W. C. Laws ◽  
A. Muszynska
Keyword(s):  
Vibration Analysis ◽  
Continuous Monitoring ◽  
Cost Effective ◽  
Predictive Maintenance ◽  
Vibration Monitoring ◽  
Alternative Methods ◽  
Monitoring Systems ◽  
High Quality ◽  
Analysis Techniques ◽  
Vibration Data

The application of vibration monitoring as part of Preventive/Predictive Maintenance programs is discussed. Several alternative methods, including periodic and continuous monitoring techniques, are described. Emphasis is given to the importance of selecting vibration transducers with due regard for the specific machinery type. The equally important need to install monitoring systems which are cost effective and provide genuinely useful information for maintenance engineers and vibration analysts is also highlighted. It is argued that critical machinery should be monitored continuously, and in cases when more detailed investigation is required that high-quality Predictive Maintenance vibration analysis techniques be applied. The need is also emphasized for specialist interpretation of vibration data in order to identify specific machinery malfunctions, of which several examples are given.

scholarly journals Vibrational Diagnostics of Rotating Machinery Malfunctions

1995 ◽  
Vol 1 (3-4) ◽  
pp. 237-266 ◽  
Author(s):  
Agnes Muszynska
Keyword(s):  
Rotating Machinery ◽  
Vibration Monitoring ◽  
Process Data ◽  
Vibration Data ◽  
Machine Monitoring ◽  
Vibration Responses ◽  
Actual Field ◽  
Vibrational Diagnostics ◽  
Monitoring And Diagnostics ◽  
Machinery Diagnostics

This paper outlines rotating machinery malfunction diagnostics using vibration data in correlation with operational process data. The advantages of vibration monitoring systems as a part of preventive/predictive maintenance programs are emphasized. After presenting basic principles of machinery diagnostics, several specific malfunction symptoms supported by simple mathematical models are given. These malfunctions include unbalance, excessive radial load, rotor-to-stator rubbing, fluid-induced vibrations, loose stationary and rotating parts, coupled torsional/lateral vibration excitation, and rotor cracking. The experimental results and actual field data illustrate the rotor vibration responses for individual malfunctions. Application of synchronous and nonsynchronous perturbation testing used for identification of basic dynamic characteristics of rotors is presented. Future advancements in vibration monitoring and diagnostics of rotating machinery health are discussed. In the Appendix, basic instrumentation for machine monitoring is outlined.

A New Model-Based Tool for Fault Detection and Isolation in Machine- and Rotordynamics

1995 ◽  
Author(s):  
Dirk Söffker
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Signal Analysis ◽  
Power Plants ◽  
Crack Detection ◽  
Fault Detection And Isolation ◽  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Implicit Assumption ◽  
Signal Parameters

Abstract Reliability and safety aspects are becoming much more important due to higher quality requirements, complicated and/or connected processes. The fault monitoring systems to be commonly used in machine- and rotordynamics are based on signal analysis methods. Furthermore, various kinds of fault detection and isolation (FDI)-schemes are already applied to a lot of technical applications of detecting and isolating sensor and actuator failures (Isermann, 1994; van Schrick, 1994) and also to fault detection in power plants (in general) or in manufacturing machines. An implicit assumption is that process or machine changes due to faults lead to changes in calculated parameters, which are unique and unambiguous. In the case of applying methods of signal analysis this means spectrums etc. the vibration behaviour will be monitored very well but have to be interpreted. On the other hand signal parameters usually only describe the system by analyzing output signals without use of known and unknown inner parameters and/or inputs. These parameters are available, and normally this knowledge is used by the operating staff interpreting the resulting signal parameters. In this way a decision-making problem appears so that questions about the physical character of faults, about the existence of special faults and also about the location of failures/faults has to be answered. In this way the experience and knowledge of the interpreting persons are very important. In this contribution the problems of the decision-making process are tried to defuse: • The available knowledge about the unfaulty system parameters is used to built up beside a nominal system model an unambiguous fault-specific ratio. Inner states of the structure are estimated by an PI-observer. • The developed robust PI-observer (Söffker et al., 1993a; Söffker et al., 1995a) estimates inner states and unknown inputs. In (Söffker et al., 1993b) this new method is applied to the crack detection of a rotor, but not proved. In this paper the proof is given and a generalization is described. The advantages in contrast to usual signal based vibration monitoring systems and also modern FDI-schemes are shown.

scholarly journals Mitigating Spikes for EMC in Vibration Monitoring Systems of Generating Plant

2013 ◽  
Vol 11 ◽  
pp. 1127-1134 ◽  
Author(s):  
Ekki Kurniawan ◽  
Deny Hamdani ◽  
Sonny Novario ◽  
Djoko Darwanto ◽  
Ngapuli I. Sinisuka
Keyword(s):  
Vibration Monitoring ◽  
Monitoring Systems

Analysis of sampling frequency in groundwater quality monitoring systems: a case study

1994 ◽  
Vol 30 (10) ◽  
pp. 73-78 ◽  
Author(s):  
Andrea Szucs ◽  
Gyözö Jordan
Keyword(s):  
Time Series ◽  
Groundwater Quality ◽  
Sampling Frequency ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Additional Information ◽  
Alarm Systems ◽  
Cross Correlation Analysis ◽  
Two Samples ◽  
Groundwater Quality Monitoring

Sampling frequency is one of the most crucial factors in the design of groundwater quality monitoring systems. Monitoring systems in general have two major objectives: (1) to describe natural processes and long-term changes and (2) to serve as alarm-systems and detect single pollution events. A comparison between two data sequences of different sampling frequency - weekly and monthly - is made through an example of the groundwater quality monitoring system in the karstic region of the Transdanubian Mountains in Hungary. Hydrogeochemical time series were first decomposed into their components: trend, periodicity, autocorrelation, and rough in succession. In order to identify outliers within the rough, Exploratory Data Analysis (EDA) was applied. Optimal sampling frequency was determined based on the analysis of the above components. Results have shown that: (1) seasons shorter than two months do exist in the studied time series which cannot be captured by monthly sampling; (2) for monitoring seasonal processes samples should be collected at the Nyquist frequency (at least two samples per period); for pollution detection autocorrelation lag-time (or semi-variogram range in time) should determine the sampling distance; in the lack of autocorrelation property the analysis of outliers should guide the sampling design; (3) cross-correlation analysis between precipitation and the observed parameters indicative of pollutant travel time yields valuable additional information on the pollution sensitivity of the hydrogeological system.

Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

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