DORA :: Browsing by Author "Mba, David"

Browsing by Author "Mba, David"

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Open Access
Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure
(Elsevier, 2018-11-24) Loukopoulosa, Panagiotis; Pilidisa, Pericles; Bennett, Ian; Zolkiewski, George; Li, Xiaochuan; Mba, David
One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the principles of diagnostics and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. SOM technique is proposed to be employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.
Open Access
Bearing signal separation enhancement with application to a helicopter transmission system
(SAGE, 2017-06-30) Elasha, Faris; Greaves, Matthew; Mba, David
Bearing vibration signal separation is essential for fault detection of gearboxes, especially where the vibration is nonstationary, susceptible to background noise, and subjected to an arduous transmission path from the source to the receiver. This paper presents a methodology for improving fault detection via a series of vibration signal processing techniques, including signal separation, synchronous averaging (SA), spectral kurtosis (SK), and envelope analysis. These techniques have been tested on experimentally obtained vibration data acquired from the transmission system of a CS-29 Category A helicopter gearbox operating under different bearing damage conditions. Results showed successful enhancement of bearing fault detection on the second planetary stage of the gearbox
Metadata only
Bearing time-to-failure estimation using spectral analysis features
(Sage, 2014-01-29) Lim, Chi Keong Reuben; Mba, David
Open Access
Canonical Variable Analysis and Long Short-term Memory for Fault Diagnosis and Performance Estimation of a Centrifugal Compressor
(Elsevier, 2018-01-03) Li, Xiaochuan; Duan, Fang; Mba, David; Bennett, Ian; Loukopoulosa, Panagiotis
Centrifugal compressors are widely used for gas lift, re-injection and transport in the oil and gas industry. Critical compressors that compress flammable gases and operate at high speeds are prioritized on maintenance lists to minimize safety risks and operational downtime hazards. Identifying incipient faults and predicting fault evolution for centrifugal compressors could improve plant safety and efficiency and reduce maintenance and operation costs. This study proposes a dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM). CVA was used to perform fault detection and identification based on the abnormalities in the canonical state and the residual space. In addition, CVA combined with LSTM was used to estimate the behavior of a system after the occurrence of a fault using data captured from the early stages of deterioration. The approach was evaluated using process data obtained from an operational industrial centrifugal compressor. The results show that the proposed method can effectively detect process abnormalities and perform multi-step-ahead prediction of the system’s behavior after the appearance of a fault.
Open Access
Canonical variate analysis, probability approach and support vector regression for fault identiﬁcation and failure time prediction
(IOS Press, 2018-06-22) Li, X.; Duan, Fang; Bennett, Ian; Mba, David
Reciprocating compressors are widely used in oil and gas industry for gas transport, lift and injection. Critical compressors that compress flammable gases and operate at high speeds are high priority equipment on maintenance improvement lists. Identifying the root causes of faults and estimating remaining usable time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. In this study, Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models are employed to identify fault related variables and predict remaining usable time based on sensory data acquired from an operational industrial reciprocating compressor. 2-D contribution plots for CVA-based residual and state spaces were developed to identify variables that are closely related to compressor faults. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and health indicators obtained from the CVA model. The trained SVR model was utilized to estimate the failure degradation rate and remaining useful life of the compressor. The results indicate that the proposed method can be effectively used in real industrial processes to perform fault diagnosis and prognosis.
Open Access
Canonical variate residuals-based contribution map for slowly evolving faults
(Elsevier, 2019-02-23) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Bennett, Ian; Collop, Andy; Mba, David
The superior performance of canonical variate analysis (CVA) for fault detection has been demonstrated by a number of researchers using simulated and real industrial data. However, applications of CVA to fault identification of industrial processes, especially for faults that evolve slowly, are not widely reported. In order to improve the performance of traditional CVA-based methods to slowly developing faults, a novel diagnostic approach is put forward to implement incipient fault diagnosis for dynamic process monitoring. Traditional CVA fault detection approach is extended to form a new monitoring index based on indices, Hotelling’s T2, Q and a canonical variate residuals (CVR)-based monitoring index Td. As an alternative to the traditional CVA-based contributions, a CVR-based contribution plot method is proposed based on Q and Td statistics. The proposed method is shown to facilitate fault detection by increasing the sensitivity to incipient faults, and aid fault identification by enhancing the contributions from fault- related variables and suppressing the contributions from fault-free variables. The CVR-based method has been demonstrated to outperform traditional CVA-based diagnostic methods for fault detection and identification when validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system and an industrial centrifugal pump.
Open Access
Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
(MDPI, 2019-02-22) Li, Xiaochuan; Mba, David; Diallo, Demba; Delpha, Claude
This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s T2, Q and a CVR-based monitoring index, Td. A CVR-based contribution plot approach is also proposed based on Q and Td statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.
Metadata only
Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions
(Elsevier, 2015-06-19) Ruiz-Carcel, Cristobal; Mba, David; Jaramillo, Victor H.; Ottewill, James R.; Cao, Yi
Open Access
Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis
(Elsevier, 2019-06-06) Li, Xiaochuan; Yang, Yingjie; Bennett, Ian; Mba, David
Condition monitoring signals obtained from rotating machines often demonstrate a highly non-stationary and transient nature due to internal natural deterioration characteristics of their constituent components and external time-varying operational conditions. Traditional multivariate statistical monitoring approaches are based on the assumption that the underlying processes are linear and static and are apt to interpret the normal changes in operating conditions as faults, which would result in high false positive rates. On the other hand, the development of robust diagnostic techniques for the detection of incipient faults remains a challenge for researchers, given the difficulty of finding an appropriate trade-off between a low false positive ratio and early detection of emerging faults. To address these issues, this paper proposes a novel adaptive fault detection approach based on the canonical residuals (CR) induced by the combination of canonical variate analysis (CVA) and matrix perturbation theory for the monitoring of dynamic processes where variations in operating conditions are incurred. The canonical residuals are calculated based upon the distinctions between past and future measurements and are able to effectively detect emerging faults while still maintaining a low false positive rate. The effectiveness of the developed diagnostic model for the detection of abnormalities in industrial processes was demonstrated for slow involving faults in case studies of two operational industrial high-pressure pumps. In comparison with the variable-based and canonical correlation-based statistical monitoring approaches, the proposed canonical residuals-adaptive canonical variate analysis (CR-ACVA) fault detection method has demonstrated its superiorities by the detailed performance comparisons.
Open Access
Evaluation of High-quality Development of Manufacturing Industry Using a Novel Grey Dynamic Double Incentive Decision-making Model
(Hindawi, 2022) Yu, Peng; Yang, Yingjie; Ma, Heng; Mba, David
This paper proposes a novel grey dynamic double incentive decision-making model to evaluate the high-quality development of manufacturing industry. First, we define the concepts of the improved grey incidence analysis and power weight Heronian aggregation (PWHA) operator. Then, we present the double incentive factors and determine incentive static evaluation values. In addition, we construct the weight vector of the time series. Guided by the incentive static evaluation values and weight vector of the time series, the dynamic evaluation values are produced. Finally, a practical example of the manufacturing industry in the Yangtze River Delta (YRD) demonstrates the effectiveness and application of the proposed model.
Open Access
A hybrid prognostic methodology for tidal turbine gearboxes
(Elsevier, 2017-07-24) Elasha, Faris; Mba, David; Master, I.; Togneri, M.; Teixeira, J. A.
Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data
Open Access
Index similarity assisted particle filter for early failure time prediction with applications to turbofan engines and compressors
(Elsevier, 2022-06-30) Li, Xiaochuan; Lin, Tianruan; Yang, Yingjie; Mba, David; Loukopoulos, Panagiotis
The particle filter (PF) has been widely studied in the prognostics’ field due to its ability to deal with nonlinear and non-stationary systems. However, there is no update of the model parameters during the prediction, preventing PF to work in its traditional way to generate accurate long-term predictions. In order to solve this problem, we put forward an improved PF that is based on a novel health index (HI) similarity matching method. This method is employed to search for similar HIs in the training library and construct an optimal “similar HI” for the system under study. Finally, the obtained HI is consistently fed into the PF to deliver precise state-of-health (SoH) estimates. The effectiveness of the proposed PF was validated on the C MAPSS datasets as well as data collected from an operational reciprocating compressor. We observed that the new similarity matching method demonstrated excellent performance in finding suitable HIs for failure time prediction. We also observed that the proposed PF framework had a superior prognostics performance over the standard PF. We obtained an averaged predictive accuracy of 96% (C-MAPSS data) and 92% (compressor data) when only the first 10% of the degradation data were used. This work highlights the promise of combining index similarity, Procrustes analysis and PF for complementing existing prognostic methods.
Embargo
Influence of Fouling on Compressor Dynamics: Experimental and Modelling Approach
(The American Society of Mechanical Engineers, 2017-07-24) Jombo, Gbanaibolou; Pecinka, Jiri; Sampath, Suresh; Mba, David
The effect of compressor fouling on the performance of a gas turbine has been the subject of several papers; however, the goal of this paper is to address a more fundamental question of the effect of fouling, which is the onset of unstable operation of the compressor. Compressor fouling experiments have been carried out on a test rig refitted with TJ100 small jet engine with centrifugal compressor. Fouling on the compressor blade was simulated with texturized paint with average roughness value of 6 microns. Compressor characteristic was measured for both the clean (baseline) and fouled compressor blades at several rotational speeds by throttling the engine with variable exhaust nozzle. A Greitzer-type compression system model has been applied based on the geometric and performance parameters of the TJ100 small jet engine test rig. Frequency of plenum pressure fluctuation, the mean disturbance flow coefficient and pressure-rise coefficient at the onset of plenum flowfield disturbance predicted by the model was compared with the measurement for both the baseline and fouled engine. Model prediction of the flowfield parameters at inception of unstable operation in the compressor showed good agreement with the experimental data. The results proved that used simple Greitzer model is suitable for prediction of the engine compressor unstable behaviour and prediction of the mild surge inception point for both the clean and the fouled compressor.
Open Access
Instantaneous failure mode remaining useful life estimation using non-uniformly sampled measurements from a reciprocating compressor valve failure
(Elsevier, 2018) Loukopoulosa, Panagiotis; Zolkiewski, George; Bennett, Ian; Sampath, Suresh; Pilidisa, Pericles; Li, X.; Mba, David
One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.
Open Access
An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data
(Sage, 2019) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Bennett, Ian; Mba, David
In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.
Open Access
Just-in-time learning based probabilistic gradient boosting tree for valve failure prognostics
(Elsevier, 2020-09-24) li, xiaochuan; Mba, David; Yang, Yingjie; Loukopoulos, Panagiotis
— Historical failure instances of a system with diversified degradation patterns will pose great challenge for prognostics. Consequently, it is challenging to accurately predict the remaining useful life (RUL) using a prognostic model trained from such data. To solve this problem, this paper proposes a just-in-time learningbased data-driven prognostic method for reciprocating compressors with diverse degradation patterns and operating modes. The proposed framework employs a just-in-time learning (JITL) scheme to deal with the stochastic nature of fault evolution and the diversity of degradation patterns. Moreover, a data-driven forecasting model that features a randomized and smoothed gradient boosting decision tree (RS-GBDT) is developed for RUL and uncertainty predictions. The effectiveness of the proposed approach was validated on temperature measurements collected from 13 valve failure cases of an industrial reciprocating compressor.
Open Access
Multidimensional prognostics for rotating machinery: A review
(SAGE, 2017-02-06) Li, Xiaochuan; Duan, Fang; Mba, David; Bennett, Ian
Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.
Open Access
A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines
(Elsevier, 2019-06-21) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Ian, Bennett; Mba, David
Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor.
Embargo
A Novel Grey Incidence Decision-making Method Based on Close Degree and Its Application in Manufacturing Industry Upgrading
(Research Information Ltd, 2020) Yu, Peng; Ma, Heng; Yang, Yingjie; Li, Xiaochuan; Mba, David
Targeting the problem of scheme ranking and indicator weighting that exist in grey incidence decision-making, a novel grey incidence decision-making method based on close degree is proposed, which can effectively distinguish evaluation results to the greatest extent. In this paper, we firstly deﬁne the concepts of the original and normative observation matrices, the vector normalization operator, and the data sequences of the positive and negative ideal systems’ behavioral characteristics. Then, the synthetic grey incidence coefficient is represented by the areas enclosed by two adjacent points between the scheme and the ideal sequences. This area is utilized to measure the proximity of two sequences in distance and their geometric similarity. On the basis of traditional weighting methods, the subjective-objective combined weighting method which is based on level difference maximization is employed to assign weights to indicators. We also provide theoretical proof that the weighting method is more reasonable and interpretable than traditional methods. Subsequently, we propose the close degree of grey incidence by employing the synthetic grey incidence coefficient and the subjective-objective combined weighting method, so that we can implement the scheme ranking. Finally, we take the evaluation of the status of manufacturing industry upgrading in the Yangtze River Delta (YRD) as a case analysis, and explore the theoretical and practical value of the proposed method.
Open Access
A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines
(Elsevier, 2019-03-05) Yang, Xiaoyu; Fang, Zhigeng; Yang, Yingjie; Mba, David; Li, Xiaochuan
The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models.

Browsing by Author "Mba, David"

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