Compound Fault Diagnosis of Centrifugal Pumps Using Vibration Analysis Techniques

Centrifugal pumps are widely used in many different industrial processes, such as power generation stations, chemical processing plants, and petroleum industries. The problem of failures in centrifugal pumps is a large concern due to its significant influence on such critical industries. Particularly, as the core, parts of a pump, bearings and the impellers are subject to different corrosions and their faults can cause major degradation of pump performances and lead to the breakdown of production. Therefore, an early detection of these types of faults would provide information to take timely preventive actions. This research investigates more effective techniques for diagnosing common faults of impellers and bearings with advanced signal analysis of surface vibration. As overall vibration responses contain a high level of broadband noises due to fluid cavities and turbulences, noise reduction is critical to developing reliable and accurate features. However, considering the modulation effect between the rotating shaft, vane passing components and any structural resonances, a modulation signal bispectrum (MSB) method is mainly used to extract these deterministic characteristics of modulations, which differs from previous researches in that the broadband vibration is often characterised with statistical methods, high frequency demodulation along spectrum analysis. Both theoretical analysis and experimental evaluation show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions. It starts with an in-depth examination of the vibration excitation mechanisms associated with each type of common pump faults including impeller leakages, impeller blockages, bearing inner race defects and bearing outrace defects. Subsequently, fault diagnosis was carried out using popular spectrum and envelope analysis, and more advanced kurtogram and MSB analysis. These methods all can successfully provide correct detection and diagnosis of the faults, which are induced manually to the test pump. Envelope analysis in a bands optimised with Kurtogram produces outstanding detection results for bearing faults but not the impeller faults in a frequency range as high as several thousand hertz (about 7.5kHz). In addition, it cannot provide satisfactory diagnostic results in separating the faults across different flow rates, especially when the compound faults were evaluated. This deficiency is because they do not have the capability of suppressing the random noises. Meanwhile, it has found that the MSB analysis allows both impeller and bearing faults to be detected and diagnosed. Especially, when the pump operated with compound faults both the fault types and severity can be attained by the analysis with acceptable accuracy for different flow rates. This high performance of diagnosis is due to that MSB has the unique capability of noise reduction and nonlinearity demodulation. Moreover, MSB diagnosis can be a frequency range lower than 2 times of the blade pass frequency (<1kHz), meaning that it can be more cost-effective as it demands lower performance measurement systems. In addition, the study also found that one accelerometer mounted on the pump housing is sufficient to monitor the faults on both the impeller and the bearing as it uses a lower frequency vibration which propagates far away from the bearing to the housing, rather than another accelerometer on the bearing pedestal directly

A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurem

The Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units

The non-intrusive detection of incipient cavitation in centrifugal pumps

This thesis investigates methods for the detection of incipient cavitation in centrifugal pumps. The thesis begins by describing the working of the centrifugal pump which makes this type of pump particularly prone to cavitation. The basic mechanisms of cavitation are described, which explain why this phenomenon is so damaging. The thesis reports the results of experiments to predict the onset cavitation using a range of statistical parameters derived from: the vibration signal obtained from an accelerometer on the pump casing, the airborne acoustic signal from a microphone close to the outlet of the pump and the waterborne acoustic signal from a hydrophone in the outlet pipe close to the pump. An assessment of the relative merits of the three methods for the detection of incipient cavitation is given based on a systematic investigation of a range of statistical parameters from time and frequency domain analysis of the signals. It is shown that is the trends in the features extracted are more than their absolute values in detecting the onset of cavitation. A number of recommendations are made as to which features are most useful, and how future work incorporating these suggestions could give a powerful method for detecting incipient cavitation. A major contribution of this research programme is the development of a novel capacitive method for the detection of cavitation. Some basic theory is presented to show the principles of the device and then the details of its construction and placement in the test rig built for the purpose. The data for the tests using the capacitive sensor are given and we can say definitely that it has been confirmed as a method of detecting cavitation in a pipe system, and that it is a promising method for the detection of the onset of cavitation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhancement of Condition Monitoring Information from the Control Data of Electrical Motors Based on Machine Learning Techniques

Centrifugal pumps are widely used in many manufacturing processes, including power plants, petrochemical industries, and water supplies. Failures in centrifugal pumps not only cause significant production interruptions but can be responsible for a large proportion of the maintenance budget. Early detection of such problems would provide timely information to take appropriate preventive actions. Currently, the motor current signature analysis (MCSA) is regarded to be a promising cost-effective condition monitoring technique for centrifugal pumps. However, conventional data analysis methods such as statistical and spectra parameters often fail to detect damage under different operating conditions, which can be attributed to the present, limited understandings of the fluctuations in current signals arising from the many different possible faults. These include the fluctuations due to changes in operating pressure and flow rate, electromagnetic interference, control accuracy and the measured signals themselves. These combine to make it difficult for conventional data analyses methods such as Fourier based analysis to accurately capture the necessary information to achieve high-performance diagnostics. Therefore, this study focuses on the improvement of data analysis through machine learning (ML) paradigms for promoting the performance of centrifugal pump monitoring. Within the paradigms, data characterisation methods such as empirical mode decomposition (EMD) and the intrinsic time-scale decomposition (ITD) reveal features based purely on the data, rather than finding pre-specified similarities to basic functions. With this data-driven approach, subtle changes are more likely to be captured and provide more effective and accurate fault detection and diagnosis. This study reports the application of two of the above data-driven approaches, using MCSA for a centrifugal pump operated under normal and abnormal conditions to detect faults seeded into the pump. The research study has shown that the use of the ITD and EMD signatures combined with envelope spectra of the current signals proved to be competent in detecting the presence of the centrifugal pump fault conditions under different flow rates. The successful analysis was able to produce a more accurate analysis of these abnormal conditions compared to conventional analytical methods. The effectiveness of these approaches is mainly due to the inclusion of high-frequency information, which is largely ignored by conventional MCSA. Finally, a comprehensive diagnostic approach is suggested based on the support vector machine (SVM) as a diagnosing method for three seeded centrifugal pump defects (two bearing defects and compound defect outer race fault with impeller blockage) under different flow rates. It is confirmed that this novel data-driven paradigm is effective for pump diagnostics. The proposed method based on a combined ITD and SVM technique for extracting meaningful features and distinguishing between seeded faults is significantly more effective and accurate for fault detection and diagnosis when compared with the results obtained from other means, such as envelope, EMD and discrete wavelet transform (DWT) based features

Condition Monitoring and Fault Diagnosis of Fluid Machines in Process Industries

Condition Monitoring (CM) of fluid machines plays a critical role in maintaining efficient productivity in many processing industries. Conventional vibration techniques generally provide more localised information with the need for many sensors, associated data acquiring and processing efforts, which are difficult for system deployment and are reluctantly accepted by those industries, for example paper mills and food production lines making marginal profits. To find adequate CM techniques for such industries this research investigates a new cost- effective scheme of implementing CM, which combines the high diagnostic capability of using Surface Vibration (SV) with the global detection capability of using the Instantaneous Angular Speed (IAS) measurements and Airborne Sound (AS). To address specific techniques involved in the scheme, this research is arranged in three consecutive Phases: Phase I is the technical evaluation; Phase II is the field implementation practices and Phase III is the application of AS through Convolution Neural Networks (CNN). In Phase I, widely used reciprocating compressor is investigated numerically and experimentally, which clarifies the performances of SV, IAS, AS, pressure and motor current in a quantitative way for differentiating common faults such as leakages happening in valves and intercoolers, faulty motor drives and mechanical transmission systems. It paves the foundations for the field implementation in Phase II. In Phase II, this novel scheme is realised on three sets of vacuum pumps in a paper mill. Based on an analytic study of dynamic responses to common faults on these pumps, a field test was conducted to verify the feasibility of the scheme and the preliminary study shows that airborne sound can show the relative spectral components for each machine to a good degree of accuracy. Knowledge gained from the preceding phases of study is now applied to Phase III. New techniques based on airborne signal differences through CNN have been demonstrated to give a good indication of the sound propagation and location of noise sources under all operating discharge pressure conditions at 100% validation accuracy, proving that the state of the art deep leaning approaches can be used to deal with complicated acoustic data

Analysis of Outer Race Bearing Damage by Calculation of Sound Signal Frequency Based on the FFT Method

This study aims to identify the outer race bearing needed to protect an induction motor from severe damage. Faults are diagnosed using a non-invasive technique through the sound signal from an induction motor. The diagnosis aims to assess the damage to the bearings on the fan or main shaft. Moreover, this study discusses the type of damage, loading variations, and the diagnostic accuracy with the damage to the outer race bearing placed on the fan or main shaft rotor. The disturbance detection approach is used to analyze the sound spectrum to identify the harmonic components near the disturbance frequency. The damage frequency characteristics are also calculated to determine the sound spectrum peak value. The results show that the detection is slightly affected by the damage severity and the incorrect placement of the bearings on the rotor shaft. The lowest detection accuracy in testing the outer race bearing damage on the fan shaft is 91.66%. However, the accuracy percentage is 100% with the outer race bearing damage on the main shaft

Vibration Signal Analysis for the Lifetime-Prediction and Failure Detection of Future Turbofan Components

Planetary gearbox and hydrodynamic journal bearings (HJB) are going to be integrated in future turbofan engines. This paper presents the results of applied methods to detect failures of these components. At first, failure detection requirements are derived by using system engineering techniques. In consideration of the identified failures theoretical assumptions are discussed and subsequently verified. Vibration and acoustic emission (AE) sensors seem promising to detect failures in an early stage. To prove the theoretical considerations experiments are carried out on test benches. Tooth flank damage of a planet gear in a planetary gearbox design is investigated. High demands are placed on the signal processing due to design-related amplitude modulation effects. Vibrations are measured using acceleration and AE sensors, which are mounted on the ring gear. The investigated failure type leads to excitation of non-stationary AE signals. It is proposed that the AE signals have a cyclostationary characteristic. Using cyclostationary-based processing techniques the signal’s hidden periodicities can be revealed. A separated analysis of each planet and evaluation of the envelope spectrum finally allows the detection of this failure type. Instead of roller bearings, HJB can be integrated in planet gears. The most essential damaging mechanism for HJB is wear as a result of mixed or boundary friction. These friction states are caused by conditions like Start/Stop Cycles, insufficient oil supply, overload or oil contamination. The accumulated intensity and duration of friction can be a measure of the remaining useful lifetime (RUL). To estimate the RUL friction has to be differentiated regarding the intensity. AE technology is a promising method to detect friction in HJB. Therefore, AE signals of the mentioned conditions are acquired. Due to rotating planet gears there is no possibility to place AE sensors directly on the surface of HJB. Finally suitable features for both components are extracted from the processed signals. Their separation efficiency with respect to the failure types is evaluated

The Optimization of Vibration Data Analysis for the Detection and Diagnosis of Incipient Faults in Roller Bearings

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect