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Journal articleTan R, Ottewill JR, Thornhill NF, 2020,
Data-driven process monitoring has benefited from the development and application of kernel transformations, especially when various types of nonlinearity exist in the data. However, when dealing with the multimodality behavior which is frequently observed in process operations, the most widely used Radial Basis Function kernel has limitations in describing process data collected from multiple normal operating modes. In this paper, we highlight this limitation via a synthesized example. In order to account for the multimodality behavior and improve fault detection performance accordingly, we propose a novel Non-stationary Discrete Convolution kernel, which derives from the convolution kernel structure, as an alternative to the RBF kernel. By assuming the training samples to be the support of the discrete convolution, this new kernel can properly address these training samples from different operating modes with diverse properties, and therefore can improve the data description and fault detection performance. Its performance is compared with RBF kernels under a standard kernel PCA framework and with other methods proposed for multimode process monitoring via numerical examples. Moreover, a benchmark data set collected from a pilot-scale multiphase flow facility is used to demonstrate the advantages of the new kernel when applied to an experimental data set.
Journal articleXuan IY, Pretlove J, Haugen T, et al., 2020,
Determining the minimum energy requirement of an LNG process: New insights into the impact of the vapour liquid equilibrium, Energy, Vol: 203, ISSN: 0360-5442
The paper provides a detailed first principles analysis of a process of liquefaction of natural gas in which the refrigeration compressors are driven by electric motors. The aim is to determine and understand the impact of the operation of the refrigeration cycles on the power consumption of the process. This study gives a detailed insight into the relationship between the discharge pressure of the compressors and the thermodynamic performance of the refrigeration cycles. By doing this, the findings of this research highlight the importance of the vapour liquid equilibrium. The results show that, for a given production rate, there is a minimum in the power consumption when the discharge pressures are close to the refrigerant saturation points. The paper provides a discussion and analysis of the advantages and disadvantages of operating at such points.
Journal articleZagorowska M, Schulze Spüntrup F, Ditlefsen A-M, et al., 2020,
Performance-based maintenance of machinery relies on detection and prediction of performance degradation. Degradation indicators calculated from process measurements need to be approximated with degradation models that smooth the variations in the measurements and give predictions of future values of the indicator. Existing models for performance degradation assume that the performance monotonically decreases with time. In consequence, the models yield suboptimal performance in performance-based maintenance as they do not take into account that performance degradation can reverse itself. For instance, deposits on the blades of a turbomachine can be self-cleaning in some conditions. In this study, a data-driven algorithm is proposed that detects if the performance degradation indicator is increasing or decreasing and adapts the model accordingly. A moving window approach is combined with adaptive regression analysis of operating data to predict the expected value of the performance degradation indicator and to quantify the uncertainty of predictions. The algorithm is tested on industrial performance degradation data from two independent offshore applications, and compared with four other approaches. The parameters of the algorithm are discussed and recommendations on the optimal choices are made. The algorithm proved to be portable and the results are promising for improving performance-based maintenance.
Journal articleTan R, Cong T, Ottewill JR, et al., 2020,
A multivariate statistical process monitoring scheme should be able to describe multimodal data. Multimodality typically arises in process data due to varying production regimes. Moreover, multimodality may influence how easy it is for process operators to interpret the monitoring results. To address these challenges, this paper proposes an on-line monitoring framework for anomaly detection where an anomaly may either indicate a fault occurring and developing in the process or the process moving to a new operating mode. The framework incorporates the Dirichlet process, which is an unsupervised clustering method, and kernel principal component analysis with a new kernel specialized for multimode data. A monitoring model is trained using the data obtained from several healthy operating modes. When on-line, if a new healthy operating mode is confirmed by an operator, the monitoring model is updated using data collected in the new mode. Implementation issues of this framework, including the parameter tuning for the kernel and the selection of anomaly indicators, are also discussed. A bivariate numerical simulation is used to demonstrate the performance of anomaly detection of the monitoring model. The ability of this framework in model updating and anomaly detection in new operating modes is shown on data from an industrial-scale process using the PRONTO benchmark dataset. The examples will also demonstrate the industrial applicability of the proposed framework.
Journal articleLucke M, Chioua M, Grimholt C, et al., 2020,
Alarm systems designed according to engineering and safety considerations provide the primary source of information for operators when it comes to abnormal situations. Still, alarm systems have rarely been exploited for fault detection and diagnosis. Recent work has demonstrated the benefits of alarm logs for fault detection and diagnosis. However, alarm settings conceived during the alarm design stage can also be integrated into fault detection and diagnosis methods. This paper suggests the use of those alarm settings in the preprocessing of the process measurements, proposing a normalization based on the alarm thresholds of each process variable. Normalization is needed to render process measurements dimensionless for multivariate analysis. While common normalization approaches such as standardization depend on the historical process measurements available, the proposed alarm-range normalization is based on acceptable variations of the process measurements. An industrial case study of an offshore oil gas separation plant is used to demonstrate that the alarm-range normalization improves the robustness of popular methods for fault detection, fault isolation, and fault identification.
Journal articleLucke M, Stief A, Chioua M, et al., 2020,
Classification-based methods for fault detection and identification can be difficult to implement in industrial systems where process measurements are subject to noise and to variability from one fault occurrence to another. This paper uses statistical alarms generated from process measurements to improve the robustness of the fault detection and identification on an industrial process. Two levels of alarms are defined according to the position of the alarm threshold: level-1 alarms (low severity threshold) and level-2 alarms (high severity threshold). Relevant variables are selected using the minimal-Redundancy-Maximal-Relevance criterion of level-2 alarms to only retain variables with large variations relative to the level of noise. The classification-based fault detection and identification fuses the results of a discrete Bayesian classifier on level-1 alarms and of a continuous Bayesian classifier on process measurements. The discrete classifier offers a practical way to deal with noise during the development of the fault, and the continuous classifier ensures a correct classification during later stages of the fault. The method is demonstrated on a multiphase flow facility.
Journal articleZhou B, Chioua M, Bauer M, et al., 2019,
Improving root cause analysis by detecting and removing transient changes in oscillatory time series with application to a 1,3-butadiene process, Industrial && Engineering Chemistry Research, Vol: 58, Pages: 11234-11250, ISSN: 0888-5885
Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection and transfer entropy method for oscillation propagation is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.
Journal articleBauer M, Auret L, Bacci di Capaci R, et al., 2019,
This paper presents the control loop data of industrial controllers that are recently made available online. All data is confirmed and some of it has been published previously to develop fault detection and diagnosis methods. Methods to detect faults that occur during the operation of an industrial process are important and have attracted attention previously but are not always widely used in industry. One of the reasons is that any method needs to be robust and fully automated. The purpose of the data repository is to present data to test methods so that false positives and negatives are reduced to an insignificant number. Three previously published methods – oscillation detection based on the autocorrelation function, the idle index and a method for quantization detection – together with a simple, novel saturation detection method and one new detection methods are applied to all industrial data. The results are discussed and ways to improve the robustness and automation potential of these methods.
Conference paperTan R, Cong T, Thornhill NF, et al., 2019,
Varying production regimes and loading conditions on equipment often result in multiple operating modes in process operations. The data recorded from such processes will typically be multimodal in nature leading to challenges in applying standard data-driven process monitoring approaches. Moreover, even if a monitoring approach is able to account for the variability present in a training set comprised of historical process data, in order to be robust and reliable the method will need to account for any new operating modes which might emerge during production. Therefore, it is desirable to have a monitoring algorithm that can both handle data multimodality in off-line training and, when implemented on-line, can actively update in order to incorporate new operating modes. This paper proposes a monitoring framework which combines an unsupervised clustering approach with a kernel-based Multivariate Statistical Process Monitoring (MSPM) algorithm. A monitoring model is trained off-line and is subsequently used to detect anomalies on-line. An anomaly might be indicative of either a developing fault or a change in the process to a new operating mode. In the latter case, the monitoring model can be updated to account for the new mode whilst still being able to detect faults under this framework. The advantages of the off-line training procedure relative to a standard kernel-based method are demonstrated via a numerical simulation. Additionally, the monitoring performance in the presence of faults and the capability of updating the model in the presence of new operating modes is demonstrated using a benchmark data set from an experimental pilot plant.
Conference paperZagorowska M, Ditlefsen A-M, Thornhill NF, et al., 2019,
Performance deterioration in turbomachinery is an unwanted phenomenon that changes the behaviour of the system. It can be described by a degradation indicator based on deviations from expected values of process variables. Existing models assume that the degradation is strictly increasing with fixed convexity and that there are no additional changes during the considered operating period. This work proposes the use of an exponential trend approximation with shape adaptation and apply it in a moving window framework. The suggested method of adjustment makes it possible for the model to follow the evolution of the indicator over time. The approximation method is then applied for monitoring purposes, to predict future degradation. The influence of the tuning parameters on the accuracy of the algorithm is investigated and recommendations for the values are derived. Finally directions for further work are proposed.
Conference paperLucke M, Mei X, Stief A, et al., 2019,
Variable selection for fault detection and identification based on mutual information of alarm series, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: International Federation of Automatic Control (IFAC), Pages: 673-678, ISSN: 1474-6670
Reducing the dimensionality of a fault detection and identification problem is often a necessity, and variable selection is a practical way to do it. Methods based on mutual information have been successful in that regard, but their applicability to industrial processes is limited by characteristics of the process variables such as their variability across fault occurrences. The paper introduces a new estimation strategy of mutual information criteria using alarm series to improve the robustness of the variable selection. The minimal-redundancy-maximal-relevance criterion on alarm series is suggested as new reference criterion, and the results are validated on a multiphase flow facility.
Journal articleLucke M, Chioua M, Grimholt C, et al., 2019,
During an alarm flood, the alarm rate is greater than the operator can effectively manage. Many alarm data analysis methods have been proposed in the literature to mitigate the impact of alarm floods. This paper gives a review of the state of the art in alarm data analysis and aims at structuring the field. A distinction between sequence mining methods that apply to alarm sequences and time series analysis methods that apply to alarm series is suggested. The review highlights that online applications to help the operators during alarm flood episodes have been only treated as sequence mining problem in the literature to date. To address this gap, the paper also presents a binary series approach to classify ongoing alarm floods based on a set of historical alarm floods. The motivation for a binary series approach is demonstrated through an industrial case study of a gas-oil separation plant, and the performance of the presented method is compared with the performance of an established sequence alignment method.
Journal articleStief A, Tan R, Cao Y, et al., 2019,
Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate data from a variety of disparate sources. Data may take a number of forms, from time-domain signals, sampled at various rates using a variety of sensors, to alarm and event logs. Novel techniques need to be developed to tackle the challenges of heterogeneous data. Testing such algorithms requires benchmark datasets that allow direct comparison of the performance of the methods. This work presents the PRONTO heterogeneous benchmark dataset. Experiments were conducted on a multiphase flow facility under various operational conditions with and without induced faults. Data were collected from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements. The presented dataset is suitable for developing and validating algorithms for fault detection and diagnosis and data fusion concepts. Three algorithms are tested using the dataset, illustrating the applicability of the dataset.
Journal articleBorghesan F, Chioua M, Thornhill NF, 2019,
Forecasting of process disturbances using k-nearest neighbours, with an application in process control, Computers and Chemical Engineering, Vol: 128, Pages: 188-200, ISSN: 1873-4375
This paper examines the prediction of disturbances based on their past measurements using k-nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model.
Journal articleCai L, Thornhill NF, Kuenzel S, et al., 2019,
This paper presents a test model for investigating how to coordinate a power grid and Energy Storage Systems (ESSs) by Wide-Area Monitoring (WAM). It consists of three parts: (1) a model of a power grid containing different types of generators, loads and transmission network; (2) a model of lithium-ion battery ESSs; (3) a model of multivariate statistical analysis based WAM built to capture the grid information for guiding the operation of ESSs. Simulation studies using a reduced equivalent model specifically built for a UK power grid enhanced with lithium-ion battery ESSs and WAM illustrate the way in which WAM can coordinate a power grid and ESSs, and also demonstrate the benefit of ESSs on a power grid.
Conference paperZagorowska M, Thornhill N, Haugen T, et al., 2018,
The objective of designing a control structure that takes the degradation of the system into account is to preserve its performance and mitigate further damage. This problem is often encountered in process industries, e.g. in gas processing plants, where the question arises how to distribute the control effort among multiple actuators based on their degradation. The main focus of this work is to investigate how to assign the loads in a two-compressor system taking the degradation, i.e. the loss of available performance, into consideration. Contrarily to other approaches, such as methods based on distance to surge or predictive control, the algorithm proposed in this work does not require a reconfiguration of the control structure, at the same time taking explicitly the degradation into account. The simulation results confirm that this approach mitigates further loss of performance, in particular for compressors, which have significantly different degradation rates.
Journal articleCai L, Thornhill NF, Kuenzel S, et al., 2018,
Wide-area monitoring of power systems using principal component analysis and k-nearest neighbor analysis, IEEE Transactions on Power Systems, Vol: 33, Pages: 4913-4923, ISSN: 0885-8950
Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
Journal articleLucke M, Chioua M, Grimholt C, et al., 2018,
Alarm systems based on engineering and safety considerations are the prime source of information for operators when it comes to abnormal situations. Conversely, the presence of fault detection and diagnosis algorithms in process plants is still limited, in comparison with other process control technologies. This work presents a simple way to integrate the information contained in the alarm systems into the fault detection and diagnosis algorithm. A normalisation of the process measurements based on the alarm thresholds is proposed, improving the robustness of the algorithm with regard to the variability of the measurements across fault occurrences in industrial systems.
Conference paperBorghesan F, Chioua M, Thornhill NF, 2018,
An MPC with disturbance forecasting for the control of the level of a tank with limited buffer capacity, Mediterranean Conference on Control and Automation (MED), Publisher: IEEE, Pages: 727-734, ISSN: 2473-3504
The paper deals with the behavior of an MPC for the control of a level of a tank, whose inflow is subject to persistent plantwide disturbances. It is shown that the response of an industrial MPC can be aggressive and oscillatory in such situations. The result is that the disturbance propagates further. The reason is the assumption made by the industrial MPCs regarding the future evolution of the disturbance. To improve the response in presence of plantwide disturbances, an MPC with disturbance forecasting is proposed. Such MPC is able to handle tight constraints and still reduce the movement of the outflow of the tank, therefore reducing the disturbance propagation. To compare the MPC with prediction forecasting with other two strategies used in industrial practice to handle measured disturbances, the paper uses sinusoidal disturbances and real disturbances coming from a refinery.
Journal articleLucke M, Chioua M, Grimholt C, et al., 2018,
Alarms indicate abnormal operation of the process plants and alarm floods constitute specific abnormal episodes that cannot be handled safely by the operators. In that regard, online alarm flood classification based on a bank of past historical episodes provides support on how to handle ongoing alarm sequences. This paper introduces a new approach based on alarm coactivations that is appropriate for the analysis of ongoing sequences. The method shows improvements when compared to an established sequence alignment approach for abnormal episode analysis of a gas oil separation plant.
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