Misalignment diagnosis of a planetary gearbox based on vibration analysis

As a critical power transmission system, planetary gearbox is widely used in many industrial important machines such as wind turbines, aircraft turbine engines, helicopters. Early fault detection and diagnosis of the gearbox will help to prevent unexpected breakdowns of this important equip-ment. Misalignment is one of the major operating problems in the planetary gearbox which may be caused by inadequate system integration, variable operating conditions and differences of elastic deformations in the system. In this paper, the effect of varying degrees of installation misalignment of planetary gearbox are investigated based on vibration measurements using spectrum analysis and modulation signal bispectrum (MSB) analysis. It has shown that the misalignment can be diagnosed in the low frequency range in which the adverse effect due to co-occurrence of amplitude modula-tion and frequency modulation (AM-FM) effect is low compared with the components around meshing frequencies. Moreover, MSB produces a more accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for all operating conditions. In contrast, spectrum can produce correct results for some of the operating conditions. Keywords: Planetary gearbox, Condition Monitoring, Misalignment, Modulation signal bispectrum

Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance

Wind turbine condition monitoring : technical and commercial challenges.

Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology

A Novel Method for the Fault Diagnosis of a Planetary Gearbox based on Residual Sidebands from Modulation Signal Bispectrum Analysis

This paper presents a novel method for the fault diagnosis of planetary gearboxes based on an accurate estimation of residual sidebands using a modulation signal bispectrum (MSB). The residual sideband resulting from the out-phase superposition of vibration waves from asymmetrical multiple meshing sources are much less influenced by gear errors than that of the in-phase sidebands. Therefore, with the accurate estimation by MSB they can produce accurate and consistent diagnosis, which are evaluated by both simulating and experimental studies. However, the commonly used in-phase sidebands have high amplitudes but include gear error effects, consequently leading to poor diagnostic results

Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain

© 2016 Juan Jose Saucedo-Dorantes et al. Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.Postprint (published version

Diagnosis of Combination Faults in a Planetary Gearbox using a Modulation Signal Bispectrum based Sideband Estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme

An Investigation of the Electrical Response of A Variable Speed Motor Drive for Mechanical Fault Diagnosis

Motor current signal analysis has been an effective way for many years to monitor electrical machines. However, little research work has been reported in using this technique for monitoring variable speed drives and their downstream equipment. This paper investigates the dynamic responses of the electrical current signals measured from a variable speed drive for monitoring the faults from a downstream gearbox. An analytical study is firstly presented in the paper to show the characteristics of the current signals due to load variation, fault effects and signal phase variation. Experimental study is then conducted under different gear fault conditions to explore the changes of the signals. Both conventional spectrum analysis and an amplitude modulation (AM) bispectrum representation are used to highlight the changes for reliable fault detection. It has been found experimentally that mechanical faults lead to much higher increases in bispectral amplitudes compared to conventional spectra and hence that detection performance of the AM bispectrum is better when the drive operates non-slip compensation mode. For slip compensation, more accurate signal analysis techniques have to be developed to differentiate the small changes in the signals

The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive

Shaft misalignments are common problems in rotating machines which cause additional dynamic and static loads, and vibrations in the system, leading to early damages and energy loss. It has been shown previously that it is possible to use motor current signature analysis to detect and diagnose this fault in motor drives. However, with a variable speed drive (VSD) system, it becomes dif-ficult to detect faults as the drive compensates for the small changes from fault ef-fects and increased noise in the measured data. In this paper, motor current signa-tures including dynamic and static data have been investigated for misalignment diagnosis in a VSD system. The study has made a systemic comparison of differ-ent control parameters between two common operation modes: open loop and sen-sorless control. Results show that fault detection features on the motor current from the sensorless mode can be the same as those of the open loop mode, however, the detection and diagnosis is significantly more difficult. In contrast, because of the additional frictional load, features from static data show results of early detection and diagnosis of different degrees of misalignment is as good as that from conventional vibration methods

Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed