Data Driven Approach to Non-stationary EMA Fault Detection and Investigation Into Remaining Useful Life

Growing interest in using Electromechanical Actuators (EMAs) to replace current hydraulic actuation methods on aircraft control surfaces has driven significant research in the area of prognostics and health management. Non- stationary speeds and loads in the course of controlling an aircraft surface make fault identification in EMAs difficult. This work presents a time- frequency analysis of EMA thrust bearing vibration signals using wavelet transforms. A relatively small EMA system is designed and built to allow for simple, quick, and repeatable component replacement. A simulated signal is developed to test four potential faults in the system. Classification is performed using an artificial neural network (ANN), which yields over 99% accuracy. Indentation faults from moderate and heavy loads are seeded in thrust bearings, which are then tested to generate data. The ANN achieves 95% classification accuracy in a two class scenario using healthy and moderately indented bearings. A three class test is executed using thrust bearings at each level of damage to perform preliminary remaining useful life (RUL) testing, where an ANN is able to identify the fault severity with an accuracy of 88%

Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence

In recent years, due to increasing requirement for reliability of industrial machines, fault diagnosis using data fusion methods has become widely applied. To recognize crucial faults of mechanical systems with high confidence, indubitably decision level fusion techniques are the foremost procedure among other data fusion methods. Therefore, in this paper in order to improve the fault diagnosis accuracy of planetary gearbox, we proposed a representative data fusion approach which exploits Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers and Dempster-Shafer (D-S) evidence theory for classifier fusion. We assumed the SVM and ANN classifiers as fault diagnosis subsystems as well. Then output values of the subsystems were regarded as input values of decision fusion level module. First, vibration signals of a planetary gearbox were captured for four different conditions of gear. Obtained signals were transmitted from time domain to time-frequency domain using wavelet transform. In next step, some statistical features of time-frequency domain signals were extracted which were used as classifiers input. The gained results of every fault diagnosis subsystem were considered as basic probability assignment (BPA) of D-S evidence theory. Classification accuracy for the SVM and ANN subsystems was determined as 80.5 % and 74.6 % respectively. Then, by using the D-S theory rules for classifier fusion, ultimate fault diagnosis accuracy was gained as 94.8 %. Results show that proposed method for vibration condition monitoring of planetary gearbox based on D-S theory provided a much better accuracy. Furthermore, an increase of more than 14 % accuracy demonstrates the strength of D-S theory method in decision fusion level fault diagnosis

Effective algorithms for real-time wind turbine condition monitoring and fault-detection

Reliable condition monitoring (CM) can be an effective means to significantly reduce wind turbine (WT) downtime, operations and maintenance costs and plan preventative maintenance in advance. The WT generator voltage and current output, if sampled at a sufficiently high rate (kHz range), can provide a rich source of data for CM. However, the electrical output of the WT generator is frequently shown to be complex and noisy in nature due to the varying and turbulent nature of the wind. Thus, a fully satisfactory technique that is capable to provide accurate interpretation of the WT electrical output has not been achieved to date. The objective of the research described in this thesis is to develop reliable WT CM using advanced signal processing techniques so that fast analysis of non-stationary current measurements with high diagnostic accuracy is achieved. The diagnostic accuracy and reliability of the proposed techniques have been evaluated using data from a laboratory test rig where experiments are performed under two levels of rotor electrical asymmetry faults. The experimental test rig was run under fixed and variable speed driving conditions to investigate the kind of results expected under such conditions. An effective extended Kalman filter (EKF) based method is proposed to iteratively track the characteristic fault frequencies in WT CM signals as the WT speed varies. The EKF performance was compared with some of the leading WT CM techniques to establish its pros and cons. The reported experimental findings demonstrate clear and significant gains in both the computational efficiency and the diagnostic accuracy using the proposed technique. In addition, a novel frequency tracking technique is proposed in this thesis to analyse the non-stationary current signals by improving the capability of a continuous wavelet transform (CWT). Simulations and experiments have been performed to verify the proposed method for detecting early abnormalities in WT generators. The improved CWT is finally applied for developing a new real-time CM technique dedicated to detect early abnormalities in a commercial WT. The results presented highlight the advantages of the improved CWT over the conventional CWT to identify frequency components of interest and cope with the non-linear and non-stationary fault features in the current signal, and go on to indicate its potential and suitability for WT CM.</div

Novelty Detection in Airport Baggage Conveyor Gear-Motors Using Synchro-Squeezing Transform and Self-Organizing Maps

A powerful continuous wavelet transform based signal processing tool named Synchro-squeezing transform (SST) has recently emerged in the context of non-stationary signal processing. Founded upon the premise of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane. Additionally, it can also extract the individual components of a non-stationary multi-component signal, which makes it attractive for rotating machinery signals. This work utilizes the decomposing power of SST transform to extract useful components from gear-motor signals in relevant sub-bands, followed by the application of standard rotating machinery condition indicators. For timely detection of faults in airport baggage conveyor gear-motors, a novelty detection technique based on the concept of self-organizing maps (SOM) is applied on the condition indicators. This approach promises improved anomaly detection performance than that can be achieved by applying condition indicators and SOM directly to the inherently complex raw-data. Data collected from conveyor gear-motors provides a test bed to demonstrate the efficacy of the proposed approach

Review of recent advances in the application of the wavelet transform to diagnose cracked rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.The authors would like to thank the Spanish government for financing through the CDTI project RANKINE21 IDI-20101560