A study of polynomial fit-based methods for qualitative trend analysis

Abstract Qualitative trend analysis (QTA) of sensor data is a useful tool for process monitoring, fault diagnosis and data mining. However, because of the varying background noise characteristics and different scales of sensor trends, automated and reliable trend extraction remains a challenge for trend-based analysis systems. In this paper, several new polynomial fit-based trend extraction algorithms are first developed, which determine the parameters automatically in the hypothesis testing framework. An existing trend analysis method developed by Dash et al. (2004) is then modified and added to the abovementioned trend extraction algorithms, which form a complete solution for QTA. The performance comparison of these algorithms is made on a set of simulated data and Tennessee Eastman process data based on several metrics.

[1]  Joaquim Melendez,et al.  Process diagnosis based on qualitative trend similarities using a sequence matching algorithm , 2014 .

[2]  Sylvie Charbonnier,et al.  A self-tuning adaptive trend extraction method for process monitoring and diagnosis , 2012 .

[3]  Raghunathan Rengaswamy,et al.  A Novel Interval-Halving Framework For Automated Identification of Process Trends , 2004 .

[4]  A qualitative shape analysis formalism for monitoring control loop performance , 2001 .

[5]  Sylvie Charbonnier,et al.  Trends extraction and analysis for complex system monitoring and decision support , 2005, Eng. Appl. Artif. Intell..

[6]  Wolfgang Marquardt,et al.  Detection of multivariable trends in measured process quantities , 2006 .

[7]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[8]  Kris Villez.  Multivariate and Qualitative Data Analysis for Monitoring, Diagnosis and Control of Sequencing Batch Reactors for Wastewater Treatment , 2007 .

[9]  Ajit C. Tamhane,et al.  Process trending with piecewise linear smoothing , 1995 .

[10]  Ahmet Palazoglu,et al.  Classification of abnormal plant operation using multiple process variable trends , 2001 .

[11]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[12]  Philip S. Yu,et al.  MALM: a framework for mining sequence database at multiple abstraction levels , 1998, CIKM '98.

[13]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[14]  Venkat Venkatasubramanian,et al.  Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis , 1991 .

[15]  Sylvie Charbonnier,et al.  On‐line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units , 2009 .

[16]  N. Lawrence Ricker,et al.  Decentralized control of the Tennessee Eastman Challenge Process , 1996 .

[17]  Philip Chan,et al.  Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[18]  Eamonn J. Keogh,et al.  Relevance feedback retrieval of time series data , 1999, SIGIR '99.

[19]  Raghunathan Rengaswamy,et al.  Fault Diagnosis by Qualitative Trend Analysis of the Principal Components , 2005 .

[20]  Raghunathan Rengaswamy,et al.  Generalized shape constrained spline fitting for qualitative analysis of trends , 2013, Comput. Chem. Eng..

[21]  A. Gabrielov,et al.  Multiscale Trend Analysis , 2004 .

[22]  Peter A. Vanrolleghem,et al.  Qualitative Representation of Trends (QRT): Extended method for identification of consecutive inflection points , 2013, Comput. Chem. Eng..

[23]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[24]  Sylvie Charbonnier,et al.  A trend-based alarm system to improve patient monitoring in intensive care units , 2007 .

[25]  Raghunathan Rengaswamy,et al.  Fault diagnosis using dynamic trend analysis: A review and recent developments , 2007, Eng. Appl. Artif. Intell..

[26]  Jim Hunter,et al.  Knowledge-Based Event Detection in Complex Time Series Data , 1999, AIMDM.

[27]  Rajagopalan Srinivasan,et al.  Monitoring transitions in chemical plants using enhanced trend analysis , 2003, Comput. Chem. Eng..

[28]  Raghunathan Rengaswamy,et al.  Fuzzy-logic based trend classification for fault diagnosis of chemical processes , 2003, Comput. Chem. Eng..

[29]  G. Stephanopoulos,et al.  Representation of process trends—Part I. A formal representation framework , 1990 .