Regularized kernel function parameter of KPCA using WPSO-FDA for feature extraction and fault recognition of gearbox

Gearbox is subject to damage or malfunctions by complicated factors such as installation position and operation condition, meanwhile, accompanied by some nonlinear behaviors, which increase the difficulty of fault diagnosis and identification. Kernel principal component analysis (KPCA) is a commonly used method to realize nonlinear mapping via kernel function for feature extraction. However, choosing an appropriate kernel function and the proper setting of its parameter are decisive to obtain a high performance of the kernel methods. In this paper, we present a novel approach combining PSO and KPCA to enhance the fault classification performance. The standard particle swarm optimization (WPSO) was used to regularize kernel function parameter of KPCA instead of the empirical value. In particular, in view of the thought of Fisher Discriminate Analysis (FDA) in pattern recognition, the optimal mathematical model of kernel parameter was constructed, and its global optimal solution was searched by WPSO. The effectiveness of the method was proven using the Iris data set classification and gearbox faults classification. In the process, gearbox fault experiments were carried out, and the vibration signals in different conditions have been tested and processed, and the fault feature parameters were extracted. At last the analysis results of gearbox fault recognition was obtained by KPCA and compared with PCA. The results show that the separability of failure patterns in the feature space is improved after kernel parameter optimized by WPSO-FDA. The problems of single failure and compound fault recognition have been effectively solved by the optimized KPCA

Monitoring and detecting faults in wastewater treatment plants using deep learning

Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults

A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring

Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications.Variational Bayesian mixture of canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises, and model discrepancies. A sequential perturbation (SP) method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide a confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a wastewater treatment plant (WWTP) simulated by benchmark simulation model with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy

Studies on crystallization process; monitoring and control

Crystallization is an oldest unit operations used by industries for the separation as well as purification of a solid product. The mathematical modelling, control and monitoring of crystallization process is a significant research area both from academic as well as industrial point of view. A temperature trajectory that improves the crystal size distribution in a batch crystallizer is proposed and compared with some of the basic cooling modes like natural, linear and controlled cooling. The properties of the crystalline product and dynamic behavior of the crystallizer, obtained by numerical experimentation are presented and analyzed here.The Mathematical modeling and simulation of a non-isothermal continuous cooling crystallizer followed by a control strategy to improve the crystal size distribution has been proposed. The model developed here is used to monitor the process using various multivariate statistical process control techniques. Present dissertation furnishes the successful implementation of various chemometric techniques. Two methods were chosen for process monitoring and control: Clustering of time series data and moving window based pattern matching. PCA similarities and dissimilarity index were chosen as the index of clustering as well as pattern matching. These methods have been successfully implemented in continuous crystallizer for fault detection and to differentiate among various operating conditions. Both the methods produced promising results

Проблемы геологии и освоения недр. Т. 2

В сборнике отражены проблемы палеонтологии, стратиграфии, тектоники, исторической и региональной геологии, минералогии, геохимии, петрологии, литологии, полезных ископаемых, металлогении, гидрогеологии, гидрогеохимии, инженерной геологии, геофизики, нефтяной геологии, геоинформационных систем в геологии, космогеологических исследований, разработки нефтяных и газовых месторождений, переработки углеводородного и минерального сырья, нефтегазопромыслового оборудования, бурения нефтяных и газовых скважин, техники и технологии разведки и добычи, транспорта и хранения нефти и газа, горного дела, технологии и техники разведки месторождений полезных ископаемых, геоэкологии, гидрогеоэкологии, охраны и инженерной защиты окружающей среды, комплексного использования минерального сырья, землеустройства, экономики минерального сырья и горного права. Публикация сборника трудов XVIII Международного научного симпозиума осуществляется при информационной поддержке Министерства образования и науки РФ (Роснаука) и при поддержке Российского фонда фундаментальных исследований РФ

Проблемы геологии и освоения недр. Т. 2

В сборнике отражены проблемы палеонтологии, стратиграфии, тектоники, исторической и региональной геологии, минералогии, геохимии, петрологии, литологии, полезных ископаемых, металлогении, гидрогеологии, гидрогеохимии, инженерной геологии, геофизики, нефтяной геологии, геоинформационных систем в геологии, космогеологических исследований, разработки нефтяных и газовых месторождений, переработки углеводородного и минерального сырья, нефтегазопромыслового оборудования, бурения нефтяных и газовых скважин, техники и технологии разведки и добычи полезных ископаемых, транспорта и хранения нефти и газа, горного дела, технологии и техники разведки месторождений полезных ископаемых, геоэкологии, гидрогеоэкологии, охраны и инженерной защиты окружающей среды, комплексного использования минерального сырья, землеустройства, экономики углеводородного и минерального сырья, горного права. Публикация сборника трудов XIX Международного научного симпозиума осуществляется при информационной поддержке Министерства образования и науки РФ (Роснаука) и при поддержке Российского фонда фундаментальных исследований РФ