Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works

In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers

Nonlinear data driven techniques for process monitoring

The goal of this research is to develop process monitoring technology capable of taking advantage of the large stores of data accumulating in modern chemical plants. There is demand for new techniques for the monitoring of non-linear topology and behavior, and this research presents a topological preservation method for process monitoring using Self Organizing Maps (SOM). The novel architecture presented adapts SOM to a full spectrum of process monitoring tasks including fault detection, fault identification, fault diagnosis, and soft sensing. The key innovation of the new technique is its use of multiple SOM (MSOM) in the data modeling process as well as the use of a Gaussian Mixture Model (GMM) to model the probability density function of classes of data. For comparison, a linear process monitoring technique based on Principal Component Analysis (PCA) is also used to demonstrate the improvements SOM offers. Data for the computational experiments was generated using a simulation of the Tennessee Eastman process (TEP) created in Simulink by (Ricker 1996). Previous studies focus on step changes from normal operations, but this work adds operating regimes with time dependent dynamics not previously considered with a SOM. Results show that MSOM improves upon both linear PCA as well as the standard SOM technique using one map for fault diagnosis, and also shows a superior ability to isolate which variables in the data are responsible for the faulty condition. With respect to soft sensing, SOM and MSOM modeled the compositions equally well, showing that no information was lost in dividing the map representation of process data. Future research will attempt to validate the technique on a real chemical process

Approaches to hazard-oriented groundwater management based on multivariate analysis of groundwater quality

Drinking water extracted near rivers in alluvial aquifers is subject to potential microbial contamination due to rapidly infiltrating river water during high discharge events. The heterogeneity of river-groundwater interaction and hydrogeological characteristics of the aquifer renders a complex pattern of groundwater quality. The quality of the extracted drinking water can be managed using decision support and HACCP (Hazard Analysis and Critical Control Point) systems, but the detection of potential contamination remains a complex task to master. The methodology proposed herein uses a combination of high-resolution measurements and multivariate statistical analyses to characterise actual groundwater quality and detect potential contamination. The aim of this project was to improve the protection of riverine groundwater extraction wells and to increase the degrees of freedom available to the management of fluvial planes with drinking-water production and aquifer recharge by river-groundwater interaction. The monitoring network was set up in the Reinacherheide in North-west Switzerland and encompassed the depth-oriented installation of multiparameter instruments, a surface-water monitoring station and a flow-through cell with an automated sampler and high-precision measurement instruments. The parameters recorded included temperature, electrical conductivity, spectral absorption coefficient, particle density and turbidity. Two of the observation wells were equipped with a telemetry system and the flow cell could be controlled remotely. The well-field encompassed eight groundwater extraction wells. The optimal choice of observation wells and indicator parameters was assessed using principal component analysis of groundwater head, temperature and electrical conductivity time-series to detect the influence of, for example, river-water infiltration or river-stage fluctuations on the time-series recorded in the groundwater observation wells. Groundwater head was susceptible to pressure waves induced by both river-stage fluctuations and groundwater extraction. Temperature time-series showed only weak responses to high discharge events. Electrical conductivity, however, showed a distance-driven response pattern to high discharge events. To further assess the representative strength of individual groundwater quality indicator parameters for identifying microbial contamination, a bi-weekly and a high-resolution sampling campaign were carried out. The results showed high faecal-indicator bacteria densities (E. coli and Enterococcus sp.) at the beginning of high discharge events, followed by a rapid decrease, leading to a strong hit-and-miss characteristic in the bi-weekly sampling campaign. The third approach applied used the neural network-based combination of self-organizing maps and Sammon's projection (SOM-SM) to detect shifts in groundwater quality system states. The nonlinear analysis was carried out with groundwater head, temperature and electrical conductivity time-series from six observation wells. The subsequent shading of the projected trajectory of system states with independent time-series (spectral absorption coefficient and particle density) allowed the identification of critical system states, when actual groundwater quality decreased and contamination of the extraction wells was imminent. The time at which the changes in system state occurred and were detected were used as potential warning indicators for the water supplier. The effects of altered groundwater extraction (as a consequence of the SOM-SM warning) were then simulated using a groundwater flow model. The outcome of the SOM-SM analysis is, thus, proposed as an interface between the monitoring system and extraction-well management system. The proposed approach incorporates hydrogeological knowledge and the analysis of prevalent conditions concerning river-groundwater interaction with real-time telemetric data transfer, data-base management and nonlinear statistical analysis to detect deterioration in actual groundwater quality due to rapidly infiltrating river water. As the SOM-SM is not based on threshold values and independent of indicator parameters, the approach can be transferred to other sites with similar characteristics

Process Monitoring and Data Mining with Chemical Process Historical Databases

Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries

This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application