An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process

Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods

Seleção de variáveis aplicada ao controle estatístico multivariado de processos em bateladas

A presente tese apresenta proposições para o uso da seleção de variáveis no aprimoramento do controle estatístico de processos multivariados (MSPC) em bateladas, a fim de contribuir com a melhoria da qualidade de processos industriais. Dessa forma, os objetivos desta tese são: (i) identificar as limitações encontradas pelos métodos MSPC no monitoramento de processos industriais; (ii) entender como métodos de seleção de variáveis são integrados para promover a melhoria do monitoramento de processos de elevada dimensionalidade; (iii) discutir sobre métodos para alinhamento e sincronização de bateladas aplicados a processos com diferentes durações; (iv) definir o método de alinhamento e sincronização mais adequado para o tratamento de dados de bateladas, visando aprimorar a construção do modelo de monitoramento na Fase I do controle estatístico de processo; (v) propor a seleção de variáveis, com propósito de classificação, prévia à construção das cartas de controle multivariadas (CCM) baseadas na análise de componentes principais (PCA) para monitorar um processo em bateladas; e (vi) validar o desempenho de detecção de falhas da carta de controle multivariada proposta em comparação às cartas tradicionais e baseadas em PCA. O desempenho do método proposto foi avaliado mediante aplicação em um estudo de caso com dados reais de um processo industrial alimentício. Os resultados obtidos demonstraram que a realização de uma seleção de variáveis prévia à construção das CCM contribuiu para reduzir eficientemente o número de variáveis a serem analisadas e superar as limitações encontradas na detecção de falhas quando bancos de elevada dimensionalidade são monitorados. Conclui-se que, ao possibilitar que CCM, amplamente utilizadas no meio industrial, sejam adequadas para banco de dados reais de elevada dimensionalidade, o método proposto agrega inovação à área de monitoramento de processos em bateladas e contribui para a geração de produtos de elevado padrão de qualidade.This dissertation presents propositions for the use of variable selection in the improvement of multivariate statistical process control (MSPC) of batch processes, in order to contribute to the enhacement of industrial processes’ quality. There are six objectives: (i) identify MSPC limitations in industrial processes monitoring; (ii) understand how methods of variable selection are used to improve high dimensional processes monitoring; (iii) discuss about methods for alignment and synchronization of batches with different durations; (iv) define the most adequate alignment and synchronization method for batch data treatment, aiming to improve Phase I of process monitoring; (v) propose variable selection for classification prior to establishing multivariate control charts (MCC) based on principal component analysis (PCA) to monitor a batch process; and (vi) validate fault detection performance of the proposed MCC in comparison with traditional PCA-based and charts. The performance of the proposed method was evaluated in a case study using real data from an industrial food process. Results showed that performing variable selection prior to establishing MCC contributed to efficiently reduce the number of variables and overcome limitations found in fault detection when high dimensional datasets are monitored. We conclude that by improving control charts widely used in industry to accomodate high dimensional datasets the proposed method adds innovation to the area of batch process monitoring and contributes to the generation of high quality standard products

Review: optical fiber sensors for civil engineering applications

Optical fiber sensor (OFS) technologies have developed rapidly over the last few decades, and various types of OFS have found practical applications in the field of civil engineering. In this paper, which is resulting from the work of the RILEM technical committee “Optical fiber sensors for civil engineering applications”, different kinds of sensing techniques, including change of light intensity, interferometry, fiber Bragg grating, adsorption measurement and distributed sensing, are briefly reviewed to introduce the basic sensing principles. Then, the applications of OFS in highway structures, building structures, geotechnical structures, pipelines as well as cables monitoring are described, with focus on sensor design, installation technique and sensor performance. It is believed that the State-of-the-Art review is helpful to engineers considering the use of OFS in their projects, and can facilitate the wider application of OFS technologies in construction industry

Avalanche Photo-Detection for High Data Rate Applications

Avalanche photo detection is commonly used in applications which require single photon sensitivity. We examine the limits of using avalanche photo diodes (APD) for characterising photon statistics at high data rates. To identify the regime of linear APD operation we employ a ps-pulsed diode laser with variable repetition rates between 0.5MHz and 80MHz. We modify the mean optical power of the coherent pulses by applying different levels of well-calibrated attenuation. The linearity at high repetition rates is limited by the APD dead time and a non-linear response arises at higher photon-numbers due to multiphoton events. Assuming Poissonian input light statistics we ascertain the effective mean photon-number of the incident light with high accuracy. Time multiplexed detectors (TMD) allow to accomplish photon- number resolution by photon chopping. This detection setup extends the linear response function to higher photon-numbers and statistical methods may be used to compensate for non-linearity. We investigated this effect, compare it to the single APD case and show the validity of the convolution treatment in the TMD data analysis.Comment: 16 pages, 5 figure

Signal Flow Graph Approach to Efficient DST I-IV Algorithms

In this paper, fast and efficient discrete sine transformation (DST) algorithms are presented based on the factorization of sparse, scaled orthogonal, rotation, rotation-reflection, and butterfly matrices. These algorithms are completely recursive and solely based on DST I-IV. The presented algorithms have low arithmetic cost compared to the known fast DST algorithms. Furthermore, the language of signal flow graph representation of digital structures is used to describe these efficient and recursive DST algorithms having $(n-1)$ points signal flow graph for DST-I and $n$ points signal flow graphs for DST II-IV

Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

Large flexible engineering structures, such as long span bridges or tall buildings, are susceptible to quasistatic and dynamic deformations caused by different loadings, thus accurate displacement measurements are desirable to assess the integrity and reliability of the structure. In this study, an integrated system that includes Global Positioning System (GPS), accelerometer and anemometer was developed to obtain the responses of a long span bridge to the extreme wind loadings. Spectral analysis based on the Fast Fourier Transform (FFT) algorithm was first carried out to detect the dominant frequencies of the middle pylon. Then the noisy GPS displacement measurements and accelerometer data are de-noised using the Vondrak filter, and the low frequency disturbance was separated from GPS displacement time series. A least-squares based displacement reconstruction scheme using noise-mitigated accelerations was employed, and the Tikhonov regularization scheme with optimal selected regularization factor was used to alleviate the ill-posedness. At last, an adaptive recursive least squares (RLS) filter was adopted to separate the slow-varying movements, and the total displacement with enhanced measurement accuracy was obtained from the combined quasi-static and high-frequency dynamic displacements. A field monitoring data set collected on the Erqi Yangtze River Bridge, a three-tower cable-stayed bridge located in Wuhan, China, was used to validate the effectiveness of the proposed integration processing scheme. The GPS/accelerometer/anemometer installed on the center supporting tower was used to characterize the interaction between the responses and the ambient wind loadings. The results demonstrate the proposed technique can significantly improve the measurement accuracy of pylon displacement under strong winds. The deformation accuracy with the amplitude of several millimeters can be successfully detected,and the spectrum of the pylon response obtained from both GPS data and accelerometer data reveals the identified first dominant frequency of the middle pylon is 0.172 Hz