Fengshou Gu

Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears

Due to excessive service load, inappropriate operating conditions or simply end of life fatigue, damage can occur in gears. When a fault, either distributed or localised, is incurred by gears, the stiffness and consequently vibration characteristics of the damaged tooth will change. A possible non-destructive technique for damage detection and severity assessment can be derived from vibration analysis. This paper presents the use of vibration analysis in the detection, quantification, and advancement monitoring of damage incurred by spur gear teeth. The stiffness of a single spur gear tooth is analysed theoretically, and due to the difficulties in measuring the gear tooth stiffness, an experimental procedure based on the modal analysis is developed to assess the severity of the gear tooth damage. A pair of spur gears was tested under accelerated wear conditions, and conventional time and frequency domain techniques are applied to the gear vibrations to indicate the presence and progression of the wear. The developed modal stiffness assessment technique is then used to quantify the resulting wear damage to the spur gear teeth.

Detection of engine valve faults by vibration signals measured on the cylinder head

Abstract This technical note proposes a simple technique for the detection of incipient engine valve faults by vibration signals measured on the cylinder head. The characteristics of the vibration signal are analysed, indicating that its time domain and frequency domain characteristics are both useful for engine diagnosis while the cycle-by-cycle variation seems a disadvantage. A simple diagnostic technique named partial sampling and feature averaging (PSFA) has been presented. Only a specific part of the vibration signal corresponding to a particular impact force within each operating cycle is sampled and analysed, and then the diagnostic features are extracted from each part but averaged among many cycles. Identification of abnormal valve clearance just requires a partial sampling of the vibration signal extracted during the period of valve closing, and detection of gas leakage from valves needs a partial sampling signal corresponding to the period of in-cylinder combustion. The experimental results show that the proposed technique is feasible, effective and simple in implementation.

Modern techniques for condition monitoring of railway vehicle dynamics

A modern railway system relies on sophisticated monitoring systems for maintenance and renewal activities. Some of the existing conditions monitoring techniques perform fault detection using advanced filtering, system identification and signal analysis methods. These theoretical approaches do not require complex mathematical models of the system and can overcome potential difficulties associated with nonlinearities and parameter variations in the system. Practical applications of condition monitoring tools use sensors which are mounted either on the track or rolling stock. For instance, monitoring wheelset dynamics could be done through the use of track-mounted sensors, while vehicle-based sensors are preferred for monitoring the train infrastructure. This paper attempts to collate and critically appraise the modern techniques used for condition monitoring of railway vehicle dynamics by analysing the advantages and shortcomings of these methods.

A Novel Transform Demodulation Algorithm for Motor Incipient Fault Detection

Faults, such as broken rotor bars, in induction motors may be detected by estimating the spectral signature of the stator currents, particularly the sidebands around the supply line frequency. However, the amplitude of the fundamental frequency (50 Hz) is considerably greater than the sideband amplitude. How to demodulate the signature frequency components under the heavy background of fundamental frequency, or how to remove the fundamental frequency, is becoming a key problem in motor current signature analysis. This paper puts forward a novel transform demodulation algorithm to solve the problem. The three-phase currents are transformed to a magnetic-torque (M-T) coordinate using this algorithm. It is found that the signature frequency components are demodulated in the magnetizing and torque-producing currents obtained by the transformation. Thus, the two demodulated M-T currents can be used to extract the enhanced signature frequency components of faults, and the incipient fault detection of induction motors is easy to realize. With both simulated and experimental data of broken rotor bars, it shows that the proposed algorithm can extract more detailed fault signature frequency components and realize the incipient fault detection of induction motors.

Non-parametric models in the monitoring of engine performance and condition: Part 2: Non-intrusive estimation of diesel engine cylinder pressure and its use in fault detection:

Abstract An application of the radial basis function model, described in Part 1, is demonstrated on a four-cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the trained model are validated against measured data and an example is given of the application of this model to the detection of a slight fault in one of the cylinders.

A novel procedure for diagnosing multiple faults in rotating machinery.

In analyzing signals from a wind turbine gearbox this paper suggests a new signal processing procedure named as CMF-EEMD method which is formed by applying conventional EEMD to a new type of combined mode function (CMF). This CMF consists of a low frequency CMF, denoted as CL, and a high frequency CMF, denoted as Ch. Then it optimizes the amplitude of the added noise in decomposing Ch and CL using EEMD. Finally, it calculates cyclic autocorrelation function (CAF) for every characteristic IMF from EEMD. The proposed procedure is applied to analyze the multi-faults of a wind turbine gearbox and the results confirm better performances in resolving different signal components by the proposed method than that from the cyclic autocorrelation function (CAF) of a direct EEMD analysis.

A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis

The problem of failures in induction motors is a large concern due to its significant influence over industrial production. Therefore a large number of detection techniques were presented to avoid this problem. This paper presents the comparison results of induction motor rotor fault detection using three methods: motor current signature analysis (MCSA), surface vibration (SV), and instantaneous angular speed (IAS). These three measurements were performed under different loads with three rotor conditions: baseline, one rotor bar broken and two rotor bar broken. The faults can be detected and diagnosed based on the amplitude difference of the characteristic frequency components of power spectrum. However IAS may be the best technique because it gives the clearest spectrum representation in which the largest amplitude change is observed due to the faults.

Diesel Engine Valve Clearance Detection Using Acoustic Emission

This paper investigated, using experimental method, the suitability of acoustic emission (AE) technique for the condition monitoring of diesel engine valve faults. The clearance fault was adjusted experimentally in an exhaust valve and successfully detected and diagnosed in a Ford FSD 425 four-cylinder, four-stroke, in-line OHV, direct injection diesel engine. The effect of faulty exhaust valve clearance on engine performance was monitored and the difference between the healthy and faulty engine was observed from the recorded AE signals. The measured results from this technique show that using only time domain and frequency domain analysis of acoustic emission signals can give a superior measure of engine condition. This concludes that acoustic emission is a powerful and reliable method of detection and diagnosis of the faults in diesel engines and this is considered to be a unique approach to condition monitoring of valve performance.

Bispectrum of stator phase current for fault detection of induction motor.

A number of research studies has shown that faults in a stator or rotor generally show sideband frequencies around the mains frequency (50 Hz) and at higher harmonics in the spectrum of the Motor Current Signature Analysis (MCSA). However in the present experimental studies such observations have not been seen, but any fault either in the stator or the rotor may distort the sinusoidal response of the motor RPM and the mains frequency so the MCSA response may contain a number of harmonics of the motor RPM and the mains frequency. Hence the use of a higher order spectrum (HOS), namely the bispectrum of the MCSA has been proposed here because it relates both amplitude and phase of number of the harmonics in a signal. It has been observed that it not only detects early faults but also indicates the severity of the fault to some extent.

A Novel Electrostatic Method of Ultrafine PM Control Suitable for Low Exhaust Temperature Applications

A novel type of electrostatic diesel particulate reduction device has been developed which is intended for use in low exhaust temperature applications. Tests were conducted to assess the performance of the technology with particular emphasis on temperature dependence and ultrafine particle removal efficiency. Rolling road dyno tests were used to enable the tests to be performed with conditions as close as possible to real on road driving. The device works by electrostatically ionizing the particulate matter which is then attracted onto an earthed surface where agglomeration occurs. This process results in a reduction in ultrafine particles which combine together with other particles to form larger agglomerates. Larger agglomerated particles are less of a health risk and are easier to remove by filtration or other means. The device was tested over a range of operating conditions on a number of vehicles and it was found in all of the tests that the device significantly reduced the emission of ultrafine particles (85 - 99%). These results were consistent over the entire drive cycle from engine cold start proving the non-temperature dependence of the technology and its suitability to low exhaust temperature applications such as urban driving where catalysis can be less effective. Conventional forecourt diesel fuel was used in all of these tests. This paper presents a selection of the test results, gives an introduction to the technology, an explanation of the ionization and agglomeration processes and details the test methods used to obtain the results.