Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
  • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
  • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

• the invention relates to faults in a process plant, and more particularly to a system and a method for tracking plant-wide faults in a process plant.
• process plants experience disturbances emanating due to the faults occurring during the process. Such disturbances could be intermittent, progressive, continuous or persistent.
• the faults in relation to causing disturbances include nonlinearities in valve, aggressive tuning, faults in sensors etc.
• Further object of the invention is to provide a method for tracking faults in a plant by a system of the invention.
• the present invention relates to a system for tracking faults in a plant.
• the system of the invention comprises detection means for detecting the faults in the plant. The detection is done continuously over a period of time suitably through corresponding measurements of the parameters of the process and of the fault related thereto.
• the system also comprises computation means for calculating at least one Key Performance Index (KPI). The KPI that is calculated is traceable over the period of time and in relation to the faults.
• Decision support means is provided to support and / or perform diagnosis based on the at least one KPI to reduce or eliminate the faults from the plant.
• the present invention also relates to a method for tracking the faults in a plant by a system in accordance with the invention.
• the method of the invention comprises the steps of detecting the faults in the plant continuously over a period of time suitably through corresponding measurements, and calculating at least one Key Performance Index (KPI).
• KPI Key Performance Index
• the KPI is traceable over a period of time and in relation to the faults.
• the method also includes providing support and / or performing diagnosis based on the at least one KPI to reduce or eliminate the faults from the plant.
• Fig. 1 shows a system for tracking faults in a plant, in accordance with the invention
• Fig. 2 shows a system for tracking faults in a plant, in accordance with an exemplary embodiment of the invention.
• Fig. 1 illustrates the system (100) for tracking faults in a plant.
• the system (100) involves a plant (101) that encounters faults (say A, B, C and D).
• the parameters of the process of the plant (101) are measured by suitable measuring means.
• the faults in the plant are detected by a detection means (102).
• These fault measurements are represented by the references XI to Xn.
• the measuring means referred herein before include tools or applications like Plant wide Disturbance Analysis (PDA).
• PDA Plant wide Disturbance Analysis
• PDA may be employed to detect the presence of oscillation clusters, and the measurements regarding the same are determined at a given instance of time.
• a computation means (103) is provided for calculating Key Performance Index (KPI) in respect of the detected faults.
• KPIs are calculated in a manner that can be tracked over a period of time. This enables the system to clearly identify the control loops that are affected by the detected faults, propagation of the fault to other control loops, appearance or occurrence of any new faults, characteristic nature of the fault (whether transient or persistent), etc, and can become part of classifying the said faults partly or holistically. This becomes viable with the tracking of the fault over a period of time, in accordance with the invention.
• a decision support means (104) is provided to support and / or perform diagnosis based on the KPI(s) to reduce or eliminate the faults from the plant. This refers to working towards reducing or eliminating the faults from the plant. In the course of providing support and / or performing diagnosis, one or more of classifying the said faults, prioritizing the said faults, reducing or eliminating the said faults or the like, are involved. This aids in appropriate and effective tracking of the faults in the plant and of reducing or eliminating the faults thereof.
• the system (200) of the invention is shown with reference to an exemplary embodiment, in accordance with the invention. Here, the plant (201) encounters the faults, and the faults are detected by the detection means (202).
• the detection means (202) detects the oscillations (202a) and clusters the oscillations (202b). Also, the detection means (202) as a Spectral Principal Component Analysis (SPCA) unit (202a') that receives the operating data from the plant (200) and reduces the dimensionality through singular value decomposition technique or the like. The results are then used for clustering the loops with similar spectral disturbance signatures obtained by SPCA clustering.
• SPCA Spectral Principal Component Analysis
• the computation means (203) calculates the KPIs in respect of the oscillations (203a) and of the disturbances (203a'). These KPIs are traceable over a period of time in respect of the faults.
• the KPIs from the previous time periods for corresponding previous or past clusters are obtained from the storage unit (204) and serves as an additional input that enables to establish the changes in the numbers / characteristics of the current or present clusters as against the previous or past clusters or vice versa.
• the KPIs so calculated are traced over a period of time or over different periods of time, by the KPI tracing unit (205). These KPIs being tracked are further used by the decision support means (206) to support or perform diagnosis to reduce or eliminate the fault from the system.
• the KPI as described above herein, for oscillation cluster may be exemplarily calculated through statistical method as follows:
• KPI abs (Pi-Pj) / max (Di, Dj)
• Pi is the mean oscillation period of cluster i
• Pj is the mean oscillation period of cluster j ;
• Di is the standard deviation of oscillation periods of cluster i.
• Dj is the standard deviation of oscillation periods of cluster j .
• the cluster i and cluster j have the same cluster identity if the KPI ⁇ 1.
• the KPIs are tracked by carrying out SPCA analysis on the plant dataset containing data from the loops for which the clusters are being compared.
• the invention is further extensible coextensively to monitoring and of tracking the faults through the progression of diagnosis of the faults in the plants. This serves as a tracking of the faults and its effect during the process of reducing or eliminating such faults.

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• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Artificial Intelligence (AREA)
• Evolutionary Computation (AREA)
• Mathematical Physics (AREA)
• Testing And Monitoring For Control Systems (AREA)

Applications Claiming Priority (2)

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Citations (5)

• 2013
  • 2013-09-27 WO PCT/IB2013/058918 patent/WO2014053971A1/fr not_active Ceased

Patent Citations (5)

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