Profitability, reliability and condition based monitoring of LNG floating platforms: a review

The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs

Improving the profitability, availability and condition monitoring of FPSO terminals

The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles

The Use of Advanced Soft Computing for Machinery Condition Monitoring

The demand for cost effective, reliable and safe machinery operation requires accurate fault detection and classification. These issues are of paramount importance as potential failures of rotating and reciprocating machinery can be managed properly and avoided in some cases. Various methods have been applied to tackle these issues, but the accuracy of those methods is variable and leaves scope for improvement. This research proposes appropriate methods for fault detection and diagnosis. The main consideration of this study is use Artificial Intelligence (AI) and related mathematics approaches to build a condition monitoring (CM) system that has incremental learning capabilities to select effective diagnostic features for the fault diagnosis of a reciprocating compressor (RC). The investigation involved a series of experiments conducted on a two-stage RC at baseline condition and then with faults introduced into the intercooler, drive belt and 2nd stage discharge and suction valve respectively. In addition to this, three combined faults: discharge valve leakage combined with intercooler leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with suction valve leakage were created and simulated to test the model. The vibration data was collected from the experimental RC and processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. A large number of potential features are calculated from the time domain, the frequency domain and the envelope spectrum. Applying Neural Networks (NNs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs) which integrate with Genetic Algorithms (GAs), and principle components analysis (PCA) which cooperates with principle components optimisation, to these features, has found that the features from envelope analysis have the most potential for differentiating various common faults in RCs. The practical results for fault detection, diagnosis and classification show that the proposed methods perform very well and accurately and can be used as effective tools for diagnosing reciprocating machinery failure

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

Proceedings of COMADEM 201

Characterising Vibro-Acoustic Signals of a Reciprocating Compressor for Condition Monitoring

Machine monitoring in industries such as chemical process plants, petroleum refineries and pulp and paper industries has significantly increased over the years, mainly because of the economic impact associated with the breakdown of a piece of equipment. With downtime sometimes costing up to 100,000 USD a day (Wachel, N.D), industrial organisations have made it mandatory to put in place systems for monitoring the condition of critical machines used for production purposes to prevent unforeseen machine breakdown. Reciprocating compressors are one of the widely used compressor types in diverse fields of application particularly in the oil and gas industry or chemical industry. In these industries, reciprocating compressors are mainly used to deliver high-pressure gas from one location to another. Due to the importance of these machines in delivering high-pressured air and sometimes toxic gases safely, their reliability has gained widespread interest over the years. To improve reciprocating compressor operational performance and reliability, this research focuses on investigating the characteristics of vibro-acoustic signals from a reciprocating compressor based on a comprehensive analysis of non-intrusive vibration measurement and discharge gas oscillations (pulsations). This study will provide more knowledge on using two techniques (vibration and gas pulsations) for online monitoring and diagnosing of reciprocating compressor faults. Other monitoring techniques such as in-cylinder pressure, instantaneous angular speed (IAS), airborne acoustic as well as vibration are previously reported in literature, however, it is believed that no information for condition monitoring of discharge gas pulsation of a reciprocating compressor has been explored. To fulfil this study, in-depth modelling and an extensive experimental evaluation for different and combined faults common to reciprocating compressor systems are explored for a wide discharge pressure range to better understand the vibro-acoustic sources. Three common faults including discharge valve leakage, intercooler leakage, discharge pipeline leakage and two combined faults: discharge valve leakage and intercooler leakage, discharge valve leakage and discharge pipeline leakage under various discharge pressures are studied in this thesis. The simulation of compressor performance with and without faults for several discharge pressures were in good agreements with the corresponding experimental evaluations, and was used to understand the compressor dynamics. Furthermore, a preliminary study on the effectiveness of conventional methods such as time-domain and frequency-domain analysis of both vibration and gas pulsation measurements were investigated. Results show that, these traditional methods were insufficient in revealing fault characteristics in the vibration signal due to the usual noise contamination and nonstationary nature of the signal. Although, with the gas pulsation signal, waveform patterns and resonant frequencies varied with faults at several discharge pressures, nevertheless, effective band pass filtering needed to identify the best frequency band that can represent the characteristic behaviour of gas pulsation signals proofed difficult and time consuming. Amongst several advanced signal-processing approaches reviewed such as wavelet transform, time synchronous average, Hilbert transform, and empirical mode decomposition; wavelet packet transform is regarded as the most powerful tool to describe gas pulsation and vibration fault signals in different frequency bands. A combination of wavelet packet transform (WPT) and Hilbert transform (envelope analysis) is proposed to achieve optimal and effective band pass filtering for resonance band identification in gas pulsation signals, and WPTs de-noising property, which can effectively reduce excessive noise revealing key transient features in vibration signals. Optimal band selection for vibration signal was achieved using entropy computation. The band with the highest entropy was used to reconstruct the signal and the envelope of the new vibration signal was used for classification. The fundamental frequency and its harmonics were used as a tool for fault classification. All fault conditions were clearly separated using the fundamental frequency and its third (3X) harmonic. Regarding gas pulsation signals, the optimal band was selected by computing the root mean square (RMS) values of all eight enveloped band signals for several discharge pressures and faults. The band with the best RMS separation trend was selected for further classification using two main diagnostic features: the kurtosis and entropy of optimal band. The plot of kurtosis against entropy as a diagnostic tool showed good valve fault classification across a wide discharge pressure range. Although the analysis of vibration signal using the proposed methods gave more reliable results for reciprocating compressor fault detection and diagnosis compared to the gas pulsation results, analysis of gas pulsation signals gave a better result on the optimal frequency band selection that can represent the behaviour of reciprocating compressor (RC) valve fault. Therefore, it can be deduced that analysis of the RC vibration signal together with the gas pulsation signal has a promising potential to be used for condition monitoring and fault diagnostics of reciprocating compressors online

Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

Centrifugal compressor machinery is subject to a potentially damaging phenomenon called surge at low mass flow rates. This effect may be preceded by a phenomena known as inlet recirculation — a flow reversal upstream of the impeller. A methodology to isolate inlet recirculation as a characteristic feature for monitoring of centrifugal compressor instability is presented in this study. The methodology is based on a nonparametric time series analysis technique called as singular spectrum analysis (SSA). SSA decomposes a signal into a number of Reconstructive Components (RCs), from which data trends and oscillatory components may be extracted. The frequency spectra of each RC and their relative contributions to the reconstruction of the original signal were examined and comparisons were made with spectral maps in existing literature. Individual and independent RCs were chosen to construct a compressor's instability monitoring system. Additionally, the performance of SSA was determined by the Window length parameter. The effect of modification of this parameter was also studied, and the various viable choices of component for the basis of inlet recirculation diagnosis were considered. The methodology was implemented in pressure dynamical signals measured in an experimental centrifugal compressor rig. High frequency pressure measurements were taken at a number of flow conditions and locations within the compressor. The results demonstrated the potential of a methodology based on SSA to identify and extract oscillatory components with information about the local effect of inlet recirculation and eventually successfully monitor centrifugal compressor's instability