Multi-layer contribution propagation analysis for fault diagnosis

The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms

Contribution plots based fault diagnosis of a multiphase flow facility with PCA-enhanced Canonical Variate Analysis

Process monitoring plays a vital role in order to sustain optimal operation and maintenance of the plant in process industry. As an essential stage in process monitoring, data-driven fault detection and diagnosis techniques have evolved quickly owing to the prosperity of multivariate feature extraction methods. In addition to the application of basic feature extraction methods, hybrid algorithms combining different methods have also been invented for better monitoring performance. In the meantime, little study has been done towards the fault diagnosis techniques under this 2-stage feature extraction framework. To deal with complex faults which will have impact on multiple process variables and the relationships among them, the Principal Component Analysis (PCA) enhanced Canonical Variate Analysis (CVA) based fault detection and diagnosis algorithm is investigated in this paper. PCA is used to pre-process the raw measurements and extracts the principal components as better indicators of process condition; CVA is conducted sequentially to further project the principal components to canonical variate space and the detection statistics are calculated based on these canonical variates. When a fault has been detected, the contributions of original process variables in monitoring statistics are derived to identify influential variables and locate the fault. To validate, along with other multivariate statistical monitoring techniques, this PCA-enhanced CVA algorithm is applied to a benchmark data set collected from an industrial scale multiphase flow facility in Cranfield University for performance evaluation

Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a cost function with certain penalties to search for the change points. The Bayesian approach is another, which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point. The paper investigates how the two approaches for change point detection can be applied to process data analytics. In addition, a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data. The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data. The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions, which will be useful for other machine learning tasks

Kernel methods for monitoring processes with multiple operating modes

Systems for monitoring processes with multiple operating modes should be able to distinguish between changes in operating modes and developing faults. Process operators will make decisions regarding production and maintenance according to the information about faults, such as the occurrence of faults and the locations of faults, so that the process can run safely and efficiently. Whilst the development and application of kernel methods can improve the performance of monitoring systems, inappropriate usage of these methods can diminish the effectiveness of the methods. In this thesis the industrial considerations of operators are summarized and these considerations are incorporated into the development of kernel methods for process monitoring. The research in the thesis shows that kernel methods need to be designed and implemented properly for monitoring processes with multiple operating modes. The research in the thesis also aims to develop kernel methods that can generate useful results when applied to monitoring of processes with multiple operating modes. The thesis reports the following research outcomes: • A benchmark multimodal dataset from a pilot-scale experiment rig; • An investigation of the tuning of kernel methods and a tuning strategy for the radial basis function kernel; • A new kernel that can improve the monitoring performance when applied to multimodal data; • An on-line monitoring framework which can account for new operating modes in the process; • A way to define the contributions of process variables to a fault detection, in order to support fault diagnosis. The thesis delivers novel kernel methods for monitoring processes with multiple operating modes and gives guidelines for proper implementation of these methods. These outcomes extend the field of process monitoring. The research outcomes are relevant for industrial application because the practical considerations of end-users are incorporated in the development of the kernel methods. The thesis also contributes to the theory of process monitoring by proposing novel kernel methods for fault detection and diagnosis. The results in the thesis demonstrate that the new development of kernel methods can improve monitoring performance when applied to processes with multiple operating modes. Moreover, the monitoring results achieved by the new kernel methods can be interpreted and used by process operators.Open Acces

Nonlinear dynamic process monitoring: the case study of a multiphase flow facility

Data-driven statistical process condition monitoring techniques have enjoyed remarkable growth over decades. However, although the source of data sets used for validating these monitoring algorithms may vary from computer simulations, experimental rigs and industrial processes, the fault being monitored are straightforward. Hence a benchmark data set acquired from an industrial-scale plant with complicated faults is of great interest. The multiphase flow facility presented in this work, which is a fully automated rig with adjustable configurations, can be a promising candidate. This paper applies a novel Multimode Kernel Latent Variable Canonical Variate Analysis (mKLV-CVA) algorithm to the benchmark data set collected from the multiphase flow facility in varying normal operating conditions as well as the slugging situation