BSc Project Fault Detection & Diagnosis in Control Valve

Transcription

1 BSc Project Fault Detection & Diagnosis in Control Valve Supervisor: Dr. Nobakhti

2 Content 2 What is fault? Why we detect fault in a control loop? What is Stiction? Comparing stiction with other faults Ways we can resolve stiction First method : Shape based (MV-OP diagram analysis) Second method : Cross Correlation Function Third method : Curve Fitting Comparing methods New method : EMD Challenges Sharif University Technology

3 What is fault? 3 SP Controller Actuator Plant Output Sensor Controller : poor tuning is not fault Actuator Fault: valve friction Plant Fault: leakage, human error Sensor Fault: calibration Sharif University Technology Fault Detection & Diagnosis in Control valve

4 Why we detect a fault? 4 A. Increasing product quality B. Reducing the rate rejection C. Fault signals propagation in physical components D. Minimizing risk instability Sharif University Technology

5 Valve Stiction 5 What is Stiction referred to? According to Horch (2000) The control valve is stuck in a certain position due to high static friction. The (integrating) controller then increases the set point to the valve until the static friction can be overcome. Then the valve breaks f and moves to a new position (slip phase) where it sticks again. The new position is usually on the other side the desired set point such that the process starts in the opposite direction again. Reference3 Sharif University Technology

6 Stiction in a control valve 6 Valve stiction is very important. The first reason is that valve stiction causes 30% loops work poorly(2 nd rank). Another reason is when a valve has stiction, the risk instability will rise. Reference 1 Schematic a control valve Reference 3 Sharif University Technology

7 Comparing stiction with Other Faults 7 Reference1 Sharif University Technology

8 MV-OP analysis: Method A,B 8 This method utilizes the fact that MV does not change even though OP changes if stiction occurs in control valves. We can quantify the degree stiction by checking the length where MV stays constant. Stiction index (SI A ) No stiction Uncertainty Stiction SI A SI A > 0.25 Reference 1 Reference 1 Sharif University Technology

9 MV-OP analysis: Method C 9 Method C is based on the qualitative shape analysis the characteristics in Fig.1 Time segments signals can be qualitatively approximated by means three qualitative symbols: increasing (I), decreasing (D) and steady (S). No stiction Unceratinty Stiction SI C SI C > 0.25 Stiction index (SI c ) Reference 1 Fig.1 Reference 1 Sharif University Technology

10 MV-OP analysis: Method A 10 Advantages They are intuitive Easy to understand Easy to implement Computationally efficient They work even when no periodical oscillation occurs Disadvantages We should have position the valve in every moment (only in smart valves) Method C is more confident than the other methods but this method also needs choosing an efficient sample time; Because lowering the sample time increases the noise & increasing the sample time is harmful. [Reference 1] Sharif University Technology

11 CCF : Cross Correlation Function 11 If the cross-correlation function (CCF) between controller output and process output is an odd function (i.e. asymmetric with respect to (w.r.t.) the vertical axis), the likely cause the oscillation is stiction. If the CCF is even (i.e. symmetric w.r.t. the vertical axis), then stiction is not likely to having caused the oscillation. a)stiction case b) Non-stiction case Reference 1 Sharif University Technology

12 CCF : Cross Correlation Function 12 a)stiction case b) Non-stiction case Reference 1 Note that the proposed method will work under the following assumptions, which will be discussed later: The process itself is not integrating (such as level control). Note that it is important that a procedure for automatic distinction between odd and even functions needs to have a deadzone. Sharif University Technology

13 CCF : Cross Correlation Function 13 Reference 1 Stiction index (SI) τ r = zero crossing for positive lags τ l = zero crossing for negative lags r 0 = CCF at lag 0 r opt = sign(r 0 ). Max(r uy (τ)) (τ [ τ 1, τ r ]) ρ = τ l τ r τ l+ τ r τ = τ r τ opt τ r+ τ opt Non stiction Uncertainty Stiction 0 ρ ρ 1/3 1/3 ρ 1 0 Δτ 1/3 1/3 Δτ 2/3 2/3 Δτ 1 Reference 1 Sharif University Technology

14 CCF : Cross Correlation Function 14 Advantages Easy implementing Using routine data No need to filtering the noise Disadvantages Not practical on integrator systems Function phase shift depends on the controller design Cross correlation function doesn t work for dominant proportional controllers [Reference 2] Sharif University Technology

15 Curve Fitting 15 To identify stiction-induced oscillations from others, we fit two different functions, triangular wave and sinusoidal wave, to the output signal the first integrating component located after the valve. OP for self-regulating processes or PV for integrating processes. A better fit to a triangular wave indicates valve stiction, while a better fit to a sinusoidal wave indicates non-stiction. Stiction index (SI) Not stiction Uncertainty Stiction SI <SI<0.6 SI 0.6 Reference 1 Sharif University Technology

16 Curve Fitting OP/PV signal fitting 16 a)sinusoidal fitting b)triangular fitting Reference 1 Sharif University Technology

17 Curve Fitting 17 Advantages One advantage is that it is applicable to both self regulating and integrating processes. Another advantage is its industrial practicability due to the following reasons: 1. The methodology is straightforward and easy to implement. 2. The detection is fully automatic and does not require user interaction. 3. Because the piecewise fit, it is flexible in handling asymmetric or damped oscillations. Disadvantages Need to know the zero crossings but because the noise it will be hard. Does not guarantee detection valve stiction in all cases. Sufficient data resolution is required for reliable diagnosis. [Reference 1] Sharif University Technology

18 Comparing 18 These methods are the most famous methods because they are : Easy to understand Easy to implement Needs routine data Rate success in analysis Curve fitting is the most efficient method among these methods. Note that no method works perfectly for all systems. Sharif University Technology

19 New Method : EMD 19 EMD or Empirical Mode Decomposition proposed by Norden E. Huang in 1998 for analyzing data from nonstationary and nonlinear processes. Reference 4 EMD decomposes any time-series signals into the sum a finite number Intrinsic Mode Functions (IMFs) EMD vs. Wavelet Analysis & Fourier Transform Conditions where the sifting process stops: 1. The residual is a monotonic function 2. The residual has less than two extrema 3. The residual is a constant Reference 4 Sharif University Technology

20 EMD Algorithm Identify all the extrema points in x(t). 2. Use cubic spline interpolation to connect all the maxima points 3. Compute the average r 1 t = (x u t + x l t )/2. 4. Compute the fastest oscillation mode (IMF) Reference 4 c 1 t = x t r 1 t We should continue the steps till the c 1 t meets the IMF conditions. 5. Once c 1 is extracted, the residual r 1 t is decomposed as r 1 t = c 2 t + r 2 t where c 2 represents the second IMF. 6. The separation into IMFs terminate when no further IMF can be extracted. Sharif University Technology

21 Sample Implementing EMD On a Signal 21 Original signal X(t) = 2 sin 2π10t + 3 sin 2πt + 0.2t 2 IMF 1 IMF 2 Residual Reference 4 Sharif University Technology

22 Sample Implementing EMD On a Signal 22 Reference 5 Sharif University Technology

23 Sample Implementing EMD On a Signal 23 Reference 5 Sharif University Technology

24 Sample Implementing EMD On a Signal 24 Reference 5 Sharif University Technology

25 EMD: Advantages and Disadvantages 25 Advantages Can be used for non-stationary and non-linear data. Uses only the output signal the process. Does not need to define a mother function like wavelet method. Disadvantages Sensitive to noise IMF s are not unique (mode mixing problem) Sharif University Technology

26 EMD: Mode Mixing Problem 26 X(t) = 2 sin 2π10t + 3 sin 2πt + 0.2t 2 Sharif University Technology

27 EMD: Mode Mixing Problem 27 X(t) = 2 sin 2π10t + 3 sin 2πt + 0.2t 2 Sharif University Technology

28 Challenges 28 Simulating the EMD method Advantages and Disadvantages How to modify the EMD? Extracting IMF specifications for Stiction detection Classification IMF Stiction Sharif University Technology

29 References 1) Jelali M, Huang B, Detection and Diagnosis Stiction in Control Loops, Springer New York, ) Horch A, Condition Monitoring Control Loops, PhD thesis, Royal Institute Technology, Stockholm, Sweden, ) M. Ale Mohammad, B. Hung, Frequency analysis and experimental validation for stiction phenomenon in multi-loop processes, University Alberta, Department Chemical and Material Engineering, Edmonton, Alberta, Canada, ) Ranganathan Srinivasana, Raghunathan Rengaswamya, Randy Miller, A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops, Department Chemical Engineering, Clarkson University, P.O. 5705, NY 13699, USA bhoneywell Process Solutions, Thousand Oaks, CA, USA, ) ECG denoising by deterministic approaches, Marjaneh Taghavi Razavizadeh, Sharif University Technology, International Campus, Kish Island,2014 Sharif University Technology

30 Questions?