Vibro-acoustic Analysis of Reciprocating Compressor in the Context of Fault Diagnosis

This project assessed the behaviour of a positive displacement type of compressor utilising airborne acoustic signatures. The study concentrated on finding an improved method based on airborne sound that can be suitable for diagnosing some common faults in reciprocating compressor (RC). Being a critical component of the industry, the condition monitoring of reciprocating compressor is very much needed to avoid any failure of its machine parts that can cause a sudden breakdown of RC. The compressor acoustic signal is a result of various mechanical forces related to the varied cylinder pressure, valve movement, turbulence air flow which in terms contribute to the periodic excitation along with the non-linearity caused by the valve fluttering, hence making the airborne signal complex and non-stationary in nature. The transient response due to the periodic impact of the valves, modulation effect due to the fluid-mechanical interaction and low signal to noise ratio (SNR) are the challenging aspects of this study. To demonstrate the vibro-acoustic property of the reciprocating compressor, first a model was developed. The leakages in valve and intercooler are very common in RC. The second most common fault which is often neglected is a clogged filter. Hence, taking into consideration, filter blockage fault is introduced for the first time in the existing test set up. Three faults (discharge valve leakage, intercooler leakage and filter blockage) are simulated, and corresponding acoustic responses are recorded for further study of the signal-nature. The model is then validated by the actual data from RC test bed. Along with the modelling of compressor acoustics, various signal processing techniques like Minimum Entropy Deconvolution (MED), Teager Energy Operator (TEO) are used on the test data to detect abnormalities present. MED in this case, is proved to be effective in finding the transient responses whereas, TEO serves as an energy detection tool for tracking the total mechanical energy. Still both methods find it difficult to come up with the best possible diagnosis results as they fail to take all the major characteristics of the RC acoustics into consideration. To overcome this challenge, higher order spectral analysis as a form of Modulation Signal Bi-Spectrum (MSB) is used to find out the most effective modulating components by enhancing the modulating characteristics and suppressing the noise. Moreover, the quadratic phase coupling allows MSB to handle the non-linearity that might be present in RC due to the valve fluttering. The proposed MSB based method not only provides a more consistent and accurate diagnosis of compressor faults but also shows that airborne acoustics has a good aspect in fault identification of RC by validating both model and test results. Recognizing that there is perpetual room for improvement, the performance of the proposed RC fault diagnosis method can be enhanced by incorporating a denoising technique developed using the Variational Mode Decomposition (VMD) associated with Kalman filtering method. The future study must also consider several other individual and compound faults that can be incorporated in the study for understanding vibro-acoustic phenomena of RC

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method

Rolling-bearing faults can be effectively reflected using time-frequency characteristics. However, there are inevitable interference and redundancy components in the conventional time-frequency characteristics. Therefore, it is critical to extract the sensitive parameters that reflect the rolling-bearing state from the time-frequency characteristics to accurately classify rolling-bearing faults. Thus, a new tensor manifold method is proposed. First, we apply the Hilbert-Huang transform (HHT) to rolling-bearing vibration signals to obtain the HHT time-frequency spectrum, which can be transformed into the HHT time-frequency energy histogram. Then, the tensor manifold time-frequency energy histogram is extracted from the traditional HHT time-frequency spectrum using the tensor manifold method. Five time-frequency characteristic parameters are defined to quantitatively depict the failure characteristics. Finally, the tensor manifold time-frequency characteristic parameters and probabilistic neural network (PNN) are combined to effectively classify the rolling-bearing failure samples. Engineering data are used to validate the proposed method. Compared with traditional HHT time-frequency characteristic parameters, the information redundancy of the time-frequency characteristics is greatly reduced using the tensor manifold time-frequency characteristic parameters and different rolling-bearing fault states are more effectively distinguished when combined with the PNN

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

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

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Characterisation of Condition Monitoring Information for Diagnosis and Prognosis using Advanced Statistical Models

This research focuses on classification of categorical events using advanced statistical models. Primarily utilised to detect and identify individual component faults and deviations from normal healthy operation of reciprocating compressors. Effective monitoring of condition ensuring optimal efficiency and reliability whilst maintaining the highest possible safety standards and reducing costs and inconvenience due to impaired performance. Variability of operating conditions being revealed through examination of vibration signals recorded at strategic points of the process. Analysis of these signals informing expectations with respect to tolerable degrees of imperfection in specific components. Isolating inherent process variability from extraneous variability affords reliable means of ascertaining system health and functionality. Vibration envelope spectra offering highly responsive model parameters for diagnostic purposes. This thesis examines novel approaches to alleviating the computational burdens of large data analysis through investigation of the potential input variables. Three methods are investigated as follows: Method one employs multivariate variable clustering to ascertain homogeneity amongst input variables. A series of heterogeneous groups being formed from each of which explanatory input variables are selected. Data reduction techniques, method two, offer an alternative means of constructing predictive classifiers. A reduced number of reconstructed explanatory variables provide enhanced modelling capabilities ensuring algorithmic convergence. The final novel approach proposed combines both these methods alongside wavelet data compression techniques. Simplifying number of input parameters and individual signal volume whilst retaining crucial information for deterministic supremacy

Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri