Plant-wide Root Cause Identification of Transient Disturbances with Application to a Board Machine

Transcription

1 Moncef Chioua, Margret Bauer, Su-Liang Chen, Jan C. Schlake, Guido Sand, Werner Schmidt and Nina F. Thornhill Plant-wide Root Cause Identification of Transient Disturbances with Application to a Board Machine CS2016, Stockholm, Sweden, 27/04/2016.

2 CONSORTIUM FIGURES PAPYRUS Project set-up Project type Timing Budget Collaborative project Co-funded by the EU 7 partners Kick off Sep months duration >3 M (ABB 50%) 1.75 M EU funding Methodology Technology Application Aalto University Universität Duisburg-Essen Universite Henri Poincare ABB Corporate Research ABB CPM Technology Predict EFORA Stora Enso

3 Outline Plant-wide disturbance root cause analysis is a large scale problem An intuitive top-down approach for decomposing this problem Extension of the method for root cause analysis of transient disturbances Case study: Board Machine 4 Stora Enso, Imatra, Finland ABB Group May 2, 2016 Slide 3

4 Plant Asset Management Traditional Approach product quality quantity efficiency Traditional Approach - Buttom-up approach - Identified critical assets asset monitors ABB Group May 2, 2016 Slide 4

5 Plant Asset Management Traditional Approach WORKFLOW product quality quantity efficiency Traditional Approach - Buttom-up approach - Identified critical assets maintenance prioritisation asset monitors Maintenance, service, inspections, etc. ABB Group May 2, 2016 Slide 5

6 Plant Asset Management Novel Approach product quality quantity efficiency PPI Plant Performance Indicator WORKFLOW Root cause analysis asset monitors Maintenance, service, inspections, etc. ABB Group May 2, 2016 Slide 6

7 Top down approach Section isolation Disturbance source isolation Diagnosis Step 1 Step 2 Step 3 Step 4 Monitor process performance indicator, e.g. Moisture Detect process performance indicator degradation T 2 contribution plot analysis for different types of measurements Isolate section containing disturbance source Measurements clustering and root cause analysis of isolated section Isolate disturbance root cause Evaluate asset performance indicator Diagnosis ABB Group May 2, 2016 Slide 7

8 Detect process performance indicator degradation One of the three end product quality ( Moisture content ) exhibits oscillations ABB Group May 2, 2016 Slide 8

9 PPI in % Detect process performance indicator degradation Detection of Moisture Oscillation PPI characteristics PPI analysis Oscillation index SP-PV... = 0.42 [-1...1], good < 0.0 Oscillation index SP... = -1 [-1...1], good < 0.0 Oscillation period... = 5250 (0...inf] seconds, good = n.a. Oscillation amplitude... = 1.77 [0...inf] % of operating range, good = n.a... Detection of critical PPI variation: Oscillation with a period 5250 seconds System triggers root cause analysis ABB Group May 2, 2016 Slide 9

10 Find process section containing disturbance source A Principal Component Analysis (PCA) Model is built using the collected process data The PCA model is able to evaluate the contribution of each tag to the detected abnormal situation In order to deal with oscillations, the PCA model uses spectral data (FFT transform of process measurements) Moisture content variability contribution to the frequential T 2 Index value Frequency (Hz) Tag index (*) CHIOUA, M., BAUER, M., CHEN, S.L., SCHLAKE, J.C., SAND, G., SCHMIDT W., and THORNHILL, N.F. Plant-Wide Root Cause Identification Using Plant Key Performance Indicators (KPIs) with Application to a Paper Machine, Control Engineering Practice, Special Issue on Industrial Practice of Fault Diagnosis and Fault Tolerant Control., /2/2016 Slide 10

11 T 2 Contribution Find process section containing disturbance source Contribution Plot using Pressure measurements Moisture Contribution Frequency (Hz) Tag Number T 2 Contribution at Caliper and Moisture peak frequency X = 220 Y = /2/2016 Slide 11

12 T 2 Contribution T 2 contribution Find process section containing disturbance source Contribution Plot using Pressure measurements = Most contributing tags to Caliper/Moisture variations T 2 Contribution at Caliper and Moisture peak frequency X = 220 Y = Moisture = PPI 4 Caliper Dryer Section contains the root 150 cause Tag 250 Index Stock prep/short circ Wire Press Calender Quality control system 5/2/2016 Slide 12

13 T 2 Contribution T 2 contribution Find Process Section containing disturbance source Contribution Plot using Flow measurements 14 = Most contributing tags to Caliper/Moisture variations T 2 Contribution at Caliper and Moisture peak frequency Moisture 6 = PPI 6 4 Caliper Dryer Section Calender 200 Wire contains the root cause Stock prep/short circ Tag Index Quality control system 5/2/2016 Slide 13

14 Isolate disturbance source Data Preprocessing: Band pass filtering of process measurements around the detected oscillation frequency 5/2/2016 Slide 14

15 Isolate disturbance source Clustering of process measurements belonging to the isolated process section 5/2/2016 Slide 15

16 Isolate disturbance source Root cause analysis using non-linearity index (*) Root cause Caliper Moisture 5/2/2016 Slide 16 (*)Thornhill, N.F., Finding the source of nonlinearity in a process with plant-wide oscillation. IEEE Transactions on Control Systems Technology, 13,

17 Isolate Disturbance Source Root cause analysis using non linearity index Causality Chain Control loop to be further diagnosed: KA4_PC0668 (Steam Pressure) 5/2/2016 Slide 17

18 pressure in MPa Diagnose isolated root cause asset Steam pressure control loop diagnosis Process value Diagnosis of the isolated faulty asset causing the process performance degradation OSCILLATION Oscillation index SP-PV... = 0.87 [-1...1], good < 0.0 Oscillation index SP... = -1 [-1...1], good < 0.0 Oscillation period... = 5230 (0...inf] seconds, good = n.a Diagnosis H5: periodic external disturbance? 0 [%] H7: valve stiction?... : 100 [%] H8: valve leakage?... : -1 [%] H9: valve size incorrect?... : -1 [%] analysis 5/2/2016 Slide 18

19 Transient disturbances For periodic signals, the T 2 contribution is evaluated at the frequency of oscillation of the KPI To extend the method to non periodic disturbances Find the frequency band containing most of the signal energy (e.g. use two first peaks of the power spectrum) Compute a weighted contribution tot 2 averaged over this frequency band. Weights are the values of the KPI normalized power spectrum

20 Top down approach Section isolation Disturbance source isolation Diagnosis Step 1 Step 2 Step 3 Step 4 Monitor process performance indicator, e.g. Moisture Detect process performance indicator degradation T 2 contribution plot analysis for different types of measurements Isolate section containing disturbance source Measurements clustering and root cause analysis of isolated section Isolate disturbance root cause Evaluate asset performance indicator Diagnosis ABB Group May 2, 2016 Slide 20

21 Detect process performance indicator degradation Transient disturbance One of the three end product quality ( moisture content ) exhibits variance increase

22 Detect process performance indicator degradation Detection of Moisture variance increase Continuous monitoring using of an observation window of length k Decision function k 2 Accept H 0 : i=1 e i 2 σ 1 2 σ2 0 σ 2 0 σ2 (log η 1 klog σ 0 ) 1 σ 1 k 2 Accept H 1 : i=1 e i 2 σ 1 2 σ 2 0 σ 2 0 σ2 (log η 0 klog σ 0 ) 1 σ 1 η 0 = β, η 1 α 1 = 1 β α α probability of false alarms β probability of missed detection. H 1 H 0 ABB Group May 2, 2016 Slide 22

23 T 2 Contribution Find process section containing disturbance source Contribution Plot using Pressure measurements T 2 Contribution at Caliper and Moisture peak frequency weighted averaged contrib plot 1.5 Most contributing tags to Moisture variations Moisture PPI Stock Wire Quality flow(i o ) Tag Index Dryer Section contains the root cause

24 T 2 Contribution Find Process Section containing disturbance source Contribution Plot using Flow measurements 1.4 T 2 Contribution at Caliper and Moisture peak frequency weighted averaged contrib plot Most contributing tags to Moisture variations Moisture = PPI pressure(i o ) Stock Wire Dryer Quality Tag index Dryer Section contains the root cause

25 Isolate disturbance source Root cause analysis using Transfer Entropy (*) Root cause Moisture (*) M. Bauer, J. W. Cox, M. H. Caveness, J. J. Downs and N. F. Thornhill, "Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy," in IEEE Transactions on Control Systems Technology, vol. 15, no. 1, pp , Jan

26 ABB Isolate disturbance source Diagnosis of the deviation in pressure loop Pressure Setpoint Manually moved

27 Conclusion Benefits & impact Top-down approach for plant-wide performance management Identification of the root cause of plant performance degradations Limited expert knowledge required Targeted maintenance Optimization of plant performance

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29 Industrial Process Analytics (*) Industrial Process Analytics has to deal with: Presentation of data as meaningful information; Transforming a flood of data into insights about the plant operation. (*) Courtesy of Prof. Nina Thornhill, ABB/RAEng Research Chair of Process Automation, Imperial College, London. ABB Group May 2, 2016 Slide 29

30 Case study: Three layer board machine Produced board quality is a performance indicator of the board machine Basis weight defect in cross direction ABB Group May 2, 2016 Slide 30

31 The large scale challenge How to find the root cause faulty asset among 7,000+ tags? ABB Group May 2, 2016 Slide 31

32 Detect process performance indicator degradation Transient Disturbance One of the three end product quality ( moisture content ) exhibits variance increase

33 ABB Detect process performance indicator degradation Transient Disturbance 1 st two peaks of the Moisture spectrum 0.03 Power Spectrum - Moisture FFT

34 Find Process Section containing disturbance source Spectral Data x data matrix of M process variables containing N equally spaced observations x = x 1 (t 1 ) x 1 (t N ) x M (t 1 ) x M (t N ) Fourier transform of the data matrix x Frequency matrix Take the absolute value Power Spectrum. X matrix where the rows are the single-sided power spectra X m at frequency f n X = X 1 (f 1 ) X 1 (f N ) X M (f 1 ) X M (f N ) As many frequency channels N as there are time samples

35 Find Process Section containing disturbance source Principal Components Analysis (PCA) PCA decomposition approximates the X matrix to reduce the complexity of the data Only R principal components (PC s) that explain most of the variability in the original data set are kept X = T R W R T + E T R : Loadings matrix W R : Scores matrix E: Errors not captured by the principal components.

36 Find Process Section containing disturbance source Hotelling s T 2 statistic Hotelling s T 2 statistic using the score column vector w n at frequency n: T n 2 = w n T D 1 w n = R r=1 w2 r,n d m D diagonal (eigenvalues d m of matrix X T X) d m expresses the variance of the rth score vector w m

37 Find Process Section containing disturbance source Spectral Contribution Plot Contribution of variable m at frequency channel n to the total T 2 at the same frequency channel n [T C 2 ] m,n = R r=1 tr,m X m (f n ) w r,n d r Focus is on a specific frequency n 1 (frequency dominating the spectrum of the KPI) [T C 2 ] m,n=n1 R = t r,m X m (f n1 ) w r,n 1 r=1 d r

38 Find Process Section containing disturbance source Weighted Spectral Contribution Plot Contribution of variable m at frequency channel n to the total T 2 at the same frequency channel n [T C 2 ] m,n = R tr,m X m (f n ) w r,n d r r=1 Focus is on a frequency band [n 1,n 2 ] (frequency band dominating the spectrum of the KPI) [T C 2 ] m,n=n1 >n 2 = 1 n 1 n 2 n 2 k=n 1 [T x KPI,k c 2 ] m,k x KPI,k normalized power spectrum of the KPI at frequency channel k The contribution of the power spectrum of the mth process variable X m over the frequency band [n 1,n 2 ] is weighted by the normalized power spectrum of the KPI at the corresponding frequencies