A Comparative Study of Various Methods of Gear Faults Diagnosis (original) (raw)

Fault Diagnosis of Gear by Vibration Analysis

2013

Unexpected production delays cause substantial costs in many branches of industry. A machine consists of many potentially defective elements, such as gears, bearings, shafts etc. During operation, each element contributes to the overall vibration of the machine. Each specific machine element has its own vibration signature. The purpose of machine vibration monitoring is to use the modified vibration signature to detect, localize, diagnose and prognosticate the defective gear so that relevant maintenance can be planned. When a defect e.g., a fatigue crack, appears in an element, e.g., a gear, its signature is modified. The dynamic response of gears is a matter of concern because of noise generation and dynamic loads. The present study is focused on the different modes failures observed in gears which are detected by using vibration based monitoring technique Fast Fourier Transform. A test rig is prepared to carry out the analysis by simulating faults in the gear and generating spectr...

Fault Diagnosis in Gears using Vibration Analysis

International Journal of Advanced Research in Science, Communication and Technology, 2024

In gearboxes, vibration stemming from load fluctuations and gear defects poses significant challenges. However, accessing and mounting vibration transducers in gearboxes can often be difficult. To address this, an experimental approach utilizing FFT analysis is employed to detect various types of gear tooth faults. By analysing vibration patterns, fluctuations in gear load gear faults can be identified effectively. This involves comparing signals from healthy and defective conditions using FFT analysis to trace sidebands of high-frequency vibrations. Validation is achieved by inputting data from an Accelerometer into LabVIEW, This comprehensive approach serves as a valuable tool for monitoring gear health under various operating conditions.

Gear fault monitoring: Comparison of vibration analysis techniques

1998

This paper deals with gear condition monitoring based on vibration analysis techniques. The detection and diagnostic capability of some of the most effective techniques are discussed and compared on the basis of experimental results, concerning a gear pair affected by a fatigue crack. In particular, the results of new approaches based on time-frequency and cyclostationarity analysis are compared against those obtained by means of the well accepted cepstrum analysis and amplitude and phase demodulation of meshing harmonics. Moreover, the sensitivity to fault severity is assessed by considering two different depths of the crack. The effect of choosing different transducer locations and different processing options are also shown. In the case of the experimental results considered in this paper, the power cepstrum is practically insensitive to the crack evolution. Conversely, the Spectral Correlation Density function is able to monitor the fault development and does not seem to be significantly influenced by the transducer position. The demodulation techniques are able to localise the damaged tooth; however, their sensitivity is strongly dependent on the proper choice of the filtering band and is affected by the transducer location. The Wavelet transform seems to be a good tool for crack detection; it is particularly effective if the residual part of the time synchronous averaged signal is processed.

Detection of Gear Fault Using Vibration Analysis

To achieve reliable and cost effective diagnosis, Motor current signature analysis is used to investigate the use of an induction motor as a transducer to indicate the faults in multistage gearbox via analyzing supply parameters such as phase current and instantaneous power. In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. This analysis system can be used for measuring the characteristics for a perfectly working gearbox and use the data as a standard for measuring faults and defects in other gearboxes. The objective of this paper is to design and diagnose fault in the gearbox using motor current analysis system at different gear operations on different loads. Steady load conditions on the gearbox are tested for current signatures during different gear operations. Also found the minimum power required to run on different gears and gear ratio. The motor current analysis system can be used further to specify mainly faults in the gear, misalignment of meshed gears, loss of contact of the gears and bearing wear.

Vibration monitoring for fault diagnosis of helicopter planetry gears

Proceedings of the 16th IFAC World Congress, 2005, 2005

In this paper, vibration data analysis techniques are investigated for fault diagnosis of helicopter planetary gears. A data pre-processing technique is introduced that achieves the same result as the commonly used Time Synchronous Averaging with much lower computational complexity since interpolation is not required. A notion of using raw vibration data instead of the Time Synchronous Averaged data is also presented that is more suitable for the analysis of vibration data produced by planetary gearboxes and for the purposes of detecting carrier plate crack fault. Based on this notion, features such as the Harmonic Index in the frequency domain and the Intra-Revolution Energy Variance in the wavelet domain are derived. The features are used as inputs to fault classifiers and are shown to detect the fault successfully based on the test data that is available.

Vibration Feature Extraction Methods for Gear Faults Diagnosis -A Review

2015

The key point of condition monitoring and fault diagnosis of gearboxes is a fault feature extraction. The study of fault feature detection in rotating machinery from vibration analysis and diagnosis has attracted sustained attention during past decades. In most cases determination of the condition of a gearbox requires study of more than one feature or a combination of several techniques. This paper attempts to survey and summarize the recent research and development of feature extraction methods for gear fault diagnosis, providing references for researchers concerning with this topic and helping them identify further research topics. First, the feature extraction methods for gear faults diagnosis are briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of feature extraction methods for gear faults diagnosis are summarized, in terms of industrial gearboxes. Finally, the open problems of...

VIBRATION ANALYSIS TECHNIQUES FOR GEARBOX DIAGNOSTIC: A REVIEW 1

Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. In this paper, a review is made of some current vibration analysis techniques used for condition monitoring in gear fault.

VIBRATION ANALYSIS TECHNIQUES FOR GEARBOX DIAGNOSTIC: A REVIEW

Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. In this paper, a review is made of some current vibration analysis techniques used for condition monitoring in gear fault.

An investigation on gearbox fault detection using vibration analysis techniques: A review

Gears are critical element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause signifi cant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration analysis has been used as a predictive maintenance procedure and as a support for machinery maintenance decisions. As a general rule, machines do not breakdown or fail without some form of warning, which is indicated by an increased vibration level. By measuring and analysing the machine's vibration, it is possible to determine both the nature and severity of the defect, and hence predict the machine's failure. The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analysing the vibration signal using different signal processing techniques. This paper presents a review of a variety of diagnosis techniques that have had demonstrated success when applied to rotating machinery, and highlights fault detection and identifi cation techniques based mainly on vibration analysis approaches. The paper concludes with a brief description of a new approach to diagnosis using neural networks, fuzzy sets, expert system and fault diagnosis based on hybrid artifi cial intelligence techniques.

TOWARD HELICOPTER GEARBOX DIAGNOSTICS FROM A SMALL NUMBER OF EXAMPLES

Mechanical Systems and Signal Processing, 2000

The United States Navy funded Westland Helicopters LTD to carry out a series of tests on the CH-46E helicopter transmission for the purpose of generating a database that can be used to evaluate diagnostic tools in general, and neural networks in particular. The database is extensive in the number of accelerometers, load conditions and diversity of faults. However, only 1}4 instances (levels) of each fault were included. Thus, the number of examples that can be extracted to train any classi"cation scheme is limited, and well below the minimum required. We argue that instead the database is best used to develop speci"c signal generation models for the CH-46E helicopter gearbox. Such models can then be used to suggest meaningful features and derive appropriate signal-processing tools. Speci"cally, we demonstrate that the meshing vibrations induced by the large collector gear located on the quill shaft are signi"cant and may interact with the vibrations induced by other elements attached to the same shaft. An appropriate model is developed and the e!ect, termed cross-gear-pair interaction, is studied using di!erent signal-processing tools.