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Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

An Investigation Into Bearing Fault Diagnostics for Condition Based Maintenance Using Band – Pass Filtering and Wavelet

Decomposition Analysis of Vibration Signals Joseph B Wood, Muhammad Ilyas Mazhar and Ian Howard

Curtin University: Department of Mechanical Engineering Perth, Australia

Abstract— Rotating machines are essential assets in many industries, and critical to the operation of these machines is the health of the rolling element bearings used to support shafts and gears. Condition based maintenance programs allow the health of machine components to be determined, and repairs scheduled at the optimum time, and prior to unexpected failure. One of the most common methods for detecting rolling element bearing faults is vibration analysis, with a number of different techniques available. This analysis compares the fault detection ability of a spectral kurtosis optimized band – pass filter analysis technique with an energy level optimized wavelet decomposition analysis, and presents a basic semi – automated process for diagnosis. Wavelet analysis proved superior in its ability to detect both localized faults and extended outer race faults, whilst band – pass filtering was limited by its lack of time–frequency resolution. The semi–automated process utilized wavelet analysis and proved successful in detecting localized bearing faults.

Keywords—Condition Based Maintenance; Bearing Fault Diagnostics; Vibration; Kurtosis; Wavelet; Envelopment

I. INTRODUCTION

Maintenance today, especially in an industrial environment, is focused heavily on minimizing downtime and the costs; both in materials and lost production that are a result of this. Critical to the successful operation of machines, vehicles and plant is their maintenance.

Maintenance regimes have typically followed either a reactive or preventative schedule, which often results in unexpected machine break downs, or unnecessary maintenance being conducted, both of which are costly and result in significant down time and lost productivity [1]. Reactive maintenance focuses on performing maintenance only when a component or machine has failed, whilst preventative maintenance involves performing regular maintenance procedures intended to extend life.

Condition based maintenance (CBM) is a process of analyzing machine and component health through non – intrusive means, and making repairs based on this information. Knowledge of component health aids in the prevention of unexpected failures, and also allows repairs to be conducted at the optimum time, rather than too early, and to allow planning and parts ordering well ahead of time [2]. The cost of maintenance can be anywhere between 15 and 60% of the operating costs of a plant [2], and hence any improvements to the maintenance process that reduces these costs is of great benefit.

A number of different condition monitoring techniques exist; used both currently and in the development and research phases. Commonly used techniques include vibration analysis [3], lubricant debris analysis [4], thermography [5] and performance parameter analysis [6]. Lubricant debris analysis is commonly used to determine the condition of motors and gearboxes [7], with extensive use on aircraft turbines and heavy mining vehicles [8]. Thermography has found success in the detection of worn components when comparing the results of similar machines, and of overloaded or faulty electrical circuits [9] [10] while performance parameter analysis is used extensively in monitoring the condition of centrifugal pump impellors[11].

Rolling element bearings (REB) form a crucial component of rotating machinery, particularly in manufacturing, resources and power industries [12] and are a part which commonly fails. REB’s can commonly be found in pumps, gearboxes, turbines and conveyors amongst other plant. Vibration analysis of bearings is the most commonly used technique for diagnosing surface spalling caused by cyclic fatigue stresses on bearing components[3, 13].

REB faults typically occur as a result of surface fatigue on either the inner or outer races, or on the rolling elements due to the cyclic stresses produced by the movement of the forces transferred by the rolling elements. Initial failure begins with localized crack propagation, which then results in small debris breaking away leaving a localized fault. Further operation of a REB in this stage of failure results in further surface fatigue and greater amounts of debris breaking away from the original crack location, leaving large extended surface faults and the end of the bearing life [14] [15].

The fatigue propagation process can be clearly seen in the following Figure, with the initial crack seen at the top, before progressing to a large distributed fault at the bottom.

2049© IEOM Society International


. Fig. 1. Example of the stages of fatigue failure and fault propagation on the surface of REB components [15]

Vibration analysis of bearings is an incredibly broad field, with numerous different competing techniques available. These techniques vary from basic, such as simple vibration magnitude criteria analysis, to the more advanced signal processing techniques such as ANN’s [16], Fuzzy Logic [17], Wavelet Analysis [18], Band – Pass Filtering [19], Cepstrum [20], Statistical Analysis [21], Empirical Mode Decomposition (EMD) [22] and Intrinsic Mode Functions (IMF) [23].

Band–pass filtering is a technique where a select band of frequencies which are excited by the impulse force generated by a rolling element striking a fault are retained, whilst all other frequencies are rejected. This is typically performed using a digital signal filter, using software such as Matlab. The identification of the optimum filter has been enhanced by the introduction of spectral kurtosis, which identifies the frequencies that exhibit the most impulsive nature [19].

Band – pass filtering processes have however been shown to be limited by the Fourier Transforms which are fundamental to their operation. The Fourier Transform approximates the signal by assuming the superposition of multiple constant amplitude and frequency sinusoids, and as such it has difficulty detecting non – stationary signals such as the decaying signals produced by fault impulses. This is a due to the Fourier Transform averaging its results over the entire signal length and hence losing all time resolution [24].

Wavelet analysis is a much more recent signal analysis technique to have been implemented in bearing diagnostics [25]. Wavelet decomposition breaks down the raw signal into scaled and translated versions of the mother wavelet, which closely approximate the raw signal at each point in time [25]. The wavelet transform is however able to approximate the signal at multiple scales at all points in time, rather than across fixed window widths as is the case in Fourier Transforms. This provides significantly greater time – frequency resolution and the ability to accurately approximate decaying signals [26].

Wavelet decomposition splits the signal into high and low frequency signals based on the scale of the wavelets. This process can then be performed again and again depending on the required level of decomposition. This process is shown in the following decomposition tree, with S representing the raw signal, Ai the “approximations” or low frequency half and Di the “details” or high frequency half of the signal.


Proceedings of the 2016 International ConfKuala Lumpur, Malaysia, March 8-10, 2016

Fig. 2. A wavelet decomposition

The signal is then reconstructed into a nlevel of decomposition, each consisting operformed, and thus the frequency of impuls

Whilst both band – pass and wavelet delittle comparison has been made between tfiltering locations. Limited studies have dirdata and differences in achievable fault signanalysis techniques will aid those involved suited to their purpose, and the difficulties analysis process has not been compared, whbe adopted by the maintenance engineering p

This analysis aims to compare the appliorder to determine which provides the cleachieved in order to automate the process.

A. Centrifugal Pump ApparatusBearing vibration data is most commonl

displacement of the system due to the multipwere sourced from both Curtin University [2experimental centrifugal pump apparatus as

Fig. 3. The centrifugal pum

ference on Industrial Engineering and Operations Manag6

n tree showing how the raw signal is broken up over four levels of decom

number of different time – domain signals, the number ofof a band of frequencies. This allows envelopment of ses to be determined and faults identified.

ecomposition techniques have been shown to be successfuthe two, nor have comparisons been made on the automrectly compared the application of both techniques on thnal clarity are unknown. Knowledge of the advantages anin condition monitoring to make informed choices as to

that they may experience. Further the ease of applicatiohich is of great importance as a simple and easy to use proprofession.

ication of both band – pass filtering analysis and wavelet earest and most consistent results, ease of use and whet

II. DATA ACQUISITION

ly measured with accelerometers due to the rapidly varyinple contributing vibration sources. The vibration data used26] and UNSW [19]. The Curtin University data was sour

s shown in Fig 3 below.

mp test apparatus used to obtain the Curtin University bearing fault data

gement

mposition [27]

f which dependant on the the filtered signal to be

ul in fault diagnosis, very matic optimization of the he same bearing vibration nd disadvantages of these o which technique is beston and robustness of the ocess if far more likely to

packet decomposition in ther optimization can be

ng velocity and d within this analysis rced from an

[26]



The test apparatus in Fig 3 consists of a centrifugal pump in which the faulty bearing is located, which is rotated by a 1:1 ratio belt connection between the AC motor output shaft and the pump shaft. The AC motor is set to rotate at a constant speed of 2100 rpm throughout all tests, which is controlled by a digital speed controller. The vibration of the pump was measured from four separate locations, with the accelerometer mounted on the bearing case being of the most importance for this analysis. By measuring the vibration from the bearing case the transmission path from the bearing to the accelerometer is minimized, which yields the greatest detection of bearing vibration and minimizes the noise generated by other components. The acceleration signals were collected and processed using an NI 9234 data acquisition device before being stored on the laptop in Matlab R2014a format.

The accelerometer locations are shown in Fig 4 below; note that accelerometer 4 is mounted on the bearing case and generates the data used in this analysis.

Fig. 4. Image of the locations of the accelerometres used to measure the centrifugal pump bearing vibration [26]

B. Bearing Faults The bearing used in the centrifugal pump is a double row ball shaft bearing with 7 rolling elements per race, a rolling

element diametre of 6.53 mm and a pitch diametre of 20 mm. Bearing faults were induced by striking the bearing with a rubber mallet as disassembly of the bearing was not possible without extensive damage. The shaft bearing is shown in Fig 5 below.

Fig. 5. Image of the centrifugal pump shaft bearing used for the Curtin University fault tests [26]



III. BEARING VIBRATION Impacts between bearing rolling elements and localized bearing faults cause low magnitude impulse forces to be

generated. These impulse forces excite the resonant frequencies of the bearing components and surrounding structure, which then decay rapidly due to the damping present in the system. Fig 6 below shows three of the most common localized REB faults; outer race, inner race and rolling element faults.

Fig. 6. REB schematic showing examples of localized faults in the inner and outer races and on a rolling element

The resonant frequencies which are exited have been shown to occur above 10 kHz by dynamic modelling of the system [19]. These resonant frequencies occur well above the influence of low frequency mechanical noise, however they are of such a low magnitude and decay so rapidly that they are difficult to detect amongst the high magnitude noise in the raw signal. This can be seen additionally in Fig. 7 as follows. It can be seen that the decibel magnitude of the frequency components below 10 kHz are almost identical between the faulty and healthy signals; however above this frequency the faulty bearing exhibits greater magnitude vibration.

Fig. 7. Frequency spectrum of both a healthy REB and a REB with an inner race fault identifying the excited resonance frequencies



The results of Fig 2 have shown that the most diagnostic information in the frequency spectrum lies above 10kHz for this particular bearing system. Therefore focusing the analysis on this region will provide the greatest ability to diagnose faults.

IV. VIBRATION ANALYSIS OF LOCALIZED FAULTS This analysis focuses on the application of two techniques, namely band–pass filtering and wavelet packet decomposition

analysis. The goal of these techniques is to filter the raw signal and extract the most useful resonant frequencies that are excited by the bearing fault.

A. Band – Pass Filtering Band – pass filtering is a technique which has been in use for some time, and as such has been investigated in great detail.

Using digital filtering processes the raw time domain signal is passed through a filter, which is designed to allow only a select band of frequencies to pass, and rejecting all others. This filter is applied to a region of frequencies which are excited by the bearing fault.

Typically the desired frequencies to filter are those which see the biggest magnitude increase between a healthy and faulty bearing. This is often impossible to determine in industrial applications as baseline readings are rarely taken.

The recent introduction of spectral kurtosis for use in determining the optimum filter region has significantly improved the application of this technique. Kurtosis is a statistical parameter which gives a measure of the impulsiveness of a set of data, whilst spectral kurtosis is the kurtosis of each frequency component and is determined using a Short Time Fourier Transform (STFT). This allows the frequencies which exhibits the most impulsive nature to be detected, which are usually the rapidly decaying resonant frequencies excited by the bearing fault. This allows the optimum filter location to be determined without the need for baseline vibration data. The use of spectral kurtosis in selecting the optimum filter location has been shown to be effective [19].

Analysis of varying filter widths between 200 and 14000 Hz of various fault signals was performed in this analysis and the results found that a width of 1000 Hz provides the best resolution of fault signals and harmonics, as such this filter width was used for all band – pass filtering analysis tests. It should be noted that this filter width provided the best results for the available data, and it is likely that different filtering widths will optimize other bearing vibration data.

Analysis of a centrifugal shaft bearing with a minor ball fault was found to yield a maximum spectral kurtosis at a frequency of 13665 Hz. Band pass filtering this signal at a frequency band of 13165 – 14165 Hz isolates the frequency information corresponding to the greatest bearing resonances. In order to determine the frequency at which these transient frequencies occur, the signal is enveloped using a Hilbert Transform. This essentially captures the overall shape of the filtered signal which results in a low frequency signal modulated by the bearing fault frequency [3].

An FFT of the enveloped signal produces a frequency spectrum containing the fault frequencies of the bearing shown as follows (Fig 8).

Fig. 8. Envelope spectrum of band – pass filtered bearing with a rolling element fault



Analysis of this frequency spectrum clearly identifies a dominant frequency component at 95 Hz. Computation of theoretical fault frequencies shows that the ball spin frequency is predicted to be 47.8850 Hz, with twice this being 95.77 Hz which would correspond to the number of rolling element fault strikes per rolling element revolution. Given that the two values are within 1 Hz of each other, which is acceptable when allowing for shaft and rolling element slip, it can be concluded from this spectrum that a rolling element fault exists.

B. Wavelet Packet Decomposition Wavelet decomposition has only recently gained the attention of engineers for bearing vibration analysis due to its

improved ability to detect decaying signals. The ability to detect signal shapes is highly dependent on the selected mother wavelet, as they each have significantly different characteristics. The Meyer wavelet has been shown to provide excellent results when detecting rapidly decaying signals as its decaying wave shape closely matches the non – stationary resonant vibrations [28] and is thus selected as the mother wavelet for this analysis.

Wavelet decomposition offers an alternative to band – pass filtering by breaking down the signal into numerous frequency groups based on the wavelet coefficients. These groups of coefficients are then reconstructed into time – domain signals containing only the frequencies approximated from the wavelet coefficients.

Envelopment of the reconstructed signals allows the fault frequencies to be detected in the same way as the band – pass filtering process. By analyzing each of the 16 frequency spectrums produced by a 4th level decomposition, the best can be selected by searching for the signal with the clearest fault harmonics and minimal noise. The best spectrum was shown to have a crest factor of 54.7243, whilst the crest factor using band – pass filtering was shown to be 46.0667. This shows that the fault signal is more defined amongst the low magnitude noise using wavelet analysis as compared to band – pass filtering. The optimum envelope spectrum of a 4th level decomposition is shown in Fig 9.

Fig. 9. Envelope spectrum of best wavelet decomposition node of a bearing with a rolling element fault

This spectrum again clearly identifies a dominant frequency component at 95 Hz, closely matching the ball spin frequency; however this spectrum also clearly identifies three harmonics. Harmonics of the fault frequency are important as they indicate that a greater number of sinusoids are needed to approximate the shape of the envelope. An increased number of harmonics shows that the decaying shape of the signal is being better approximated by the final Fourier Transform. It can also be seen that the magnitude of the frequencies below the 95 Hz component have been reduced when compared with band – pass filtering, this shows that the fault information has been better isolated from other non – diagnostic noise.



V. SEMI – AUTOMATED FAULT DIAGNOSIS

Selecting the best spectrum based on expert knowledge however becomes time consuming and difficult when a greater level of decomposition is applied as an exponentially larger number of signals are produced with each increase in level. In much the same way as kurtosis has been used to select an appropriate band for filtering the measure of a wavelet reconstructed signals energy has been shown to be an excellent predictor of diagnostic information.

This process has been completed by ignoring the energy levels of all signals which are reconstructions of the low frequency vibrations, as these always contain significantly more energy due to their mechanical sources when compared with the relatively small vibrations generated by bearing faults. A high level of energy will occur where the acceleration values are high, within the high frequency region this will occur where bearing resonances are excited (Zhou 2012). By only analyzing the high frequency signal with the highest energy the process can be automated, and the need for an expert eye eliminated.

A basic semi – automated Matlab code has been developed which analyzes a given bearing signal for faults using only input data of the bearing specifications, operating speed and sampling frequency. The user inputs are used to calculate the theoretical characteristic fault frequencies whilst the wavelet analysis process is computed automatically. If the dominant fault component occurs within 1.5 Hz of any of the theoretical fault frequencies a fault source is displayed to the user. Fig 10 below shows a flow chart of the process.

Fig. 10. The process used for the semi – automed diagnosis function represented in a flow chart

User Select Input Signal

User Input Bearing Specifications and Test Parameters

Calculate Characteristic Fault Frequencies

Complete Wavelet Packet Decomposition

Select Decomposed Signal to Analyze Based on Highest Energy of High Frequency Nodes

Envelope Selected Signal

Perform FFT of Enveloped Signal

Determine Maximum of Frequency Spectrum

Check for Input Error

Check if Maximum Lies Within Allowable Bounds of Characteristic Frequencies

If a Match Exists Display Error Source to User

Display Analyzed Frequency Spectrum for Secondary Analysis



The application of the semi-automated process has significantly decreased the amount of time required to analyze raw bearing signals. In order to test the effectiveness of this method, all available data was tested from both Curtin University and UNSW, with each localized fault being identified successfully. This process has shown excellent ability in diagnosing localized bearing faults.

VI. VIBRATION ANALYSIS OF EXTENDED FAULTS Detecting extended faults usually proves difficult when using envelopment techniques as the impulses are less periodic and

buried amongst a greater amount of noise. Analysis of distributed outer and inner race faults has shown some success using the semi – automated technique with the outer race fault being detected clearly as the highest peak in the spectrum. The envelope spectrum obtained for the distributed fault is shown in Fig 11 below.

Fig. 11. Frequency spectrum of distributed outer race fault indicating the presence of a fault frequency and mechanical noise

Fig 9 clearly shows the dominant component exists at 49.8 Hz, which corresponds to a theoretical ball pass frequency of 49.07 Hz. This spectrum also detects the presence of both the shaft rotational speed and the gear mesh frequency of the system,

Distributed outer race faults were however more difficult to isolate and did not appear as the maximum value in the frequency spectrum, rather appearing at a lower magnitude than the shaft speed. This is likely due to the movement of the distributed fault in and out of the bearing load zone, resulting in amplitude modulation by the shaft speed. This shows that the fault induced resonance frequencies are not being as well isolated from the non – diagnostic noise as is the case with localized faults.

VII. VALIDATION OF RESULTS In order to validate the results of this analysis the band – pass filtering and wavelet decomposition techniques were applied

to bearing data obtained from the University of New South Wales. This data contained acceleration signals from a test rig utilized in the doctoral thesis “Diagnostics, Prognostics and Fault Simulation for Rolling Element Bearings” by Nader Sawalhi in 2007. Available data included localized inner race, outer race and rolling element faults, created using electro – spark erosion.

Each fault was easily identifiable using both techniques, with significantly greater detection of the fault frequency and its harmonics achieved. This is was due to the sharp and highly artificial nature of the etched faults, which resulted in stronger and more impulsive faults being generated. As a further example the crest factor for the optimum filter using band – pass filtering and spectral kurtosis of the UNSW bearing with an outer race fault was 40.4, when using wavelet decomposition and selecting the best spectrum the crest factor increased to 62.5, showing a 54.8% increase in the ratio of the fault frequency to the spectrum noise.



VIII. DISCUSSION

The results of this investigation into the application of band – pass filtering and wavelet analysis in detecting rolling element bearing faults for condition monitoring have shown that both techniques are highly capable of detecting localized faults on the inner race, outer race and rolling element surfaces. Limitations and advantages exist for both techniques, and will be discussed as follows.

Application of the band – pass filtering technique has been significantly improved with the use implementation of spectral kurtosis for optimizing the filtering location. This allows the signal to be filtered optimally without a baseline healthy measurement, which is difficult to obtain in practical situations. The determination of the spectral kurtosis is however inconsistent due to the fixed time – frequency resolution of the windows and Fourier Transforms used in its computation. Varying the window width for Short Time Fourier Transform spectral kurtosis computation varied the location of the maximum kurtosis, this made determining the correct filtering location difficult to determine exactly and consistently.

Also contributing to this inconsistency is the width of the band - pass window selected. Whilst analysis of available bearing data showed that 1000 Hz provided the best results, this value will likely differ significantly between tests. In order to further improve this technique further data is needed in order to either further validate the selected 1000 Hz width, or to determine a method for selecting the best width for each situation.

Wavelet packet decomposition proved significantly easier to apply than band – pass filtering as there are no issues associated with time – frequency resolution. The only decision which needs to be made during the process is which mother wavelet to use, and how many levels of decomposition are required. During the course of this analysis it was found that the best results were obtained when decomposing the signal to at least 3 levels, and to no more than 5 levels. It was found that below 3 levels the envelope spectrums contained a significant amount of noise, this is due to the wide range of frequencies which are contained within each reconstructed signal, much the same as using a high – pass or wide band – pass filter. Decomposing the signal beyond 5 levels showed inconsistent results, with some signals still detecting clear faults in the envelope spectrums, whilst others no longer detected the fault signals.

When comparing the results obtained by both techniques it is seen that wavelet analysis has provided greater fault frequency clarity. Clarity of results in the frequency domain is a combination of signal to noise ratio, presence of high magnitude mechanical noise peaks and number of clearly identifiable fault frequency harmonics. Comparing both figure 6 and figure 7 shows that the best achievable clarity using band – pass filtering is lower than the greatest clarity achieved using wavelet decomposition, this is further confirmed by the lower crest factor of 46.1 than the 54.7 obtained with wavelet analysis.

Interestingly the best wavelet spectrum did not correspond to the same frequency band as that determined by the maximum spectral kurtosis, rather it contained significantly lower magnitude vibrations. This shows that analysis of the largest resonance frequencies may not isolate the fault characteristics as well as filtering at a lower magnitude location which contains less noise.

Analysis of distributed bearing faults in both the inner and outer races have shown mixed success using both band – pass filtering and wavelet decomposition analysis. Band – pass filtering showed limited diagnosis ability as no fault signatures were detected for the inner race fault, and some outer race fault signatures detected however amongst a large amount of high magnitude noise.

Wavelet decomposition analysis provided superior fault diagnosis of distributed bearing faults. The fault frequency of the outer race ball pass frequency was easily identifiable in the envelope spectrum, and the highest magnitude component. The inner race fault spectrum showed the presence of the fault frequency and a number of its harmonics; however these were amongst a large amount of high magnitude noise.

Whilst analyzing the wavelet decomposed signal in the high frequency region of the highest energy has not been shown to produce the optimum envelope spectrum, it has however been shown to easily detect fault signals. This has allowed the process to be semi – automated which significantly increases the ease of use and applicability of this condition monitoring method. The semi – automated process has been shown to consistently diagnose the presence of localized outer race, inner race and rolling element faults in both Curtin and UNSW bearings using only bearing specifications, rotating speed and accelerometer sampling rate as inputs.

By incorporating the complex diagnosis process into a simple to use function, bearing faults can be diagnosed by a wider range of people, with limited knowledge of signal processing techniques. Given that one of the greatest issues limiting the implementation of condition monitoring is the time consumption and expert knowledge required, this diagnosis function improves the practicality of bearing vibration analysis significantly.



IX. CONCLUSION

This investigation has shown that bearing faults can be identified through the use of band –pass filtering and wavelet packet decomposition analysis techniques. The use of spectral kurtosis has shown improvements in selecting the optimum band – pass filtering location whilst a width of 1000 Hz has been shown to provide the best compromise between noise and detectable harmonics.

Wavelet packet decomposition analysis has been shown to provide superior detection ability of both localized and distributed bearing faults to band – pass filtering, with a greater number of harmonics and lower amount of noise visible. Both techniques however have difficulty in diagnosing extended faults when compared with small localized faults, indicating the importance of regular condition monitoring and thus early fault detection in critical machines.

By selecting the highest energy decomposition of the wavelet details for analysis a semi – automated diagnosis function has been presented which has been successful in diagnosing all available localized faults. These results and analysis show that vibration analysis of faulty bearings can be successfully implemented in a condition based maintenance program in order to prevent unexpected failures, optimize bearing life and plan maintenance in advance.

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BIOGRAPHY

Joseph Wood is a graduate Mechanical Engineer of Curtin University. Joseph has worked for Bradken as a mechanical engineer in both Foundry and Manufacturing environments, with the majority of his work involving plant productivity and safety upgrades. Joseph is particularly interested in reliability and process optimization, with an aim to work in these areas in the near future. He is a member of Engineers Australia and the Society for Underwater Technology.

Muhammad Ilyas Mazhar is a lecturer at the Department of Mechanical Engineering, School of Civil & Mechanical Engineering, Curtin University, Perth, Australia. Mr Mazhar received his PhD in Manufacturing Engineering and Management from the University of New South Wales. Prior to that, he worked as a professional mechanical engineer for more than ten years. His research interests include condition monitoring, reliability engineering and life cycle assessment.

Ian Howard has worked at Curtin University since 1994 in the Department of Mechanical Engineering. Prior to that he was a research scientist with DSTO at the Aeronautical Research Laboratory, Melbourne from 1988. His research has largely focused on the use of vibration for the detection of incipient failure of rotating machinery, including gears, bearings and applications including wind turbines and in the development of novel sensors.


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