Loop status monitoring and fault localisation

Abstract Loop status monitoring involves the declaration of deterministic trends, such as oscillations and drifting, in loops that are in multi-loop plant configurations. By analysing various time domain statistics pertaining to PI/PID control loops, a trend can be recognised as one of seven categories. The scientific basis for working with the particular statistics is given and the categorisation process is described. These statistics can be combined to produce an Overall Loop Performance Index for each loop, which can be compared to localise a single fault in a multi-loop arrangement. Estimation methods for these time domain statistics are outlined and the performance of the approach is demonstrated on both simulated and real plant data sets.

[1]  C. T. Seppala,et al.  A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .

[2]  Krister Forsman,et al.  A new criterion for detecting oscillations in control loops , 1999, 1999 European Control Conference (ECC).

[3]  Nina F. Thornhill,et al.  ANALYSIS OF PLANT-WIDE DISTURBANCES THROUGH DATA-DRIVEN TECHNIQUES AND PROCESS UNDERSTANDING , 2002 .

[4]  Bhavik R. Bakshi,et al.  Representation of process trends—IV. Induction of real-time patterns from operating data for diagnosis and supervisory control , 1994 .

[5]  Nina F. Thornhill,et al.  Refinery-wide control loop performance assessment , 1999 .

[6]  Alexander Horch.  A simple method for detection of stiction in control valves , 1999 .

[7]  C. T. Seppala,et al.  Recent Developments in Controller Performance Monitoring and Assessment Techniques , 2002 .

[8]  Jd Landes,et al.  Recent Developments in J Ic Testing , 1977 .

[9]  Derrick J. Kozub.  CONTROLLER PERFORMANCE MONITORING AND DIAGNOSIS. INDUSTRIAL PERSPECTIVE , 2002 .

[10]  Nina F. Thornhill,et al.  Detection of multiple oscillations in control loops , 2003 .

[11]  John Howell,et al.  Controller Output Based, Single Number Statistics for Loop Status Monitoring , 2001 .

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

[13]  Tore Hägglund,et al.  Automatic detection of sluggish control loops , 1999 .

[14]  Biao Huang,et al.  Spectral principal component analysis of dynamic process data , 2002 .

[15]  Biao Huang,et al.  Performance Assessment of Control Loops , 1999 .

[16]  Chen Gang,et al.  An improved base control for the Tennessee Eastman problem , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[17]  Tore Hägglund,et al.  A Control-Loop Performance Monitor , 1995 .

[18]  T. Miao,et al.  Automatic detection of excessively oscillatory feedback control loops , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

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

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

[21]  Chunming Xia,et al.  Loop Status Statistics , 2001 .

[22]  Bhavik R. Bakshi,et al.  Representation of process trends—III. Multiscale extraction of trends from process data , 1994 .