T.-C.J. Aravanis, J.S. Sakellariou and S.D. Fassois

Transcription

1 Vibration based fault detection under variable non-measurable, operating conditions via a stochastic Functional Model method and application to railway vehicle suspensions T.-C.J. Aravanis, J.S. Sakellariou and S.D. Fassois Stochastic Mechanical Systems & Automation (SMSA) Laboratory Department of Mechanical Engineering and Aeronautics University of Patras, GR Patras, Greece {aravanis, sakj, fassois}@mech.upatras.gr Surveillance 9, International Conference mai 2017 Fès (Maroc) Surveillance 9, Fès, May /15

2 Talk Outline Talk Outline 1. Introduction 2. The fault detection method 3. Application to railway suspensions 4. Fault detection performance assessment 5. Concluding remarks Surveillance 9, Fès, May /15

3 1. Introduction The General Problem 1. Introduction The General Problem Vibration-based fault detection under variable, non-measurable, operation conditions Why is it important? Safety Proper maintenance Comfort assurance The Challenge Structures operate under variable operating conditions Wind Payload Boundary conditions Temperature Variable, nonmeasurable operating conditions Masking of the effects of faults on the dynamics Challenging Fault detection (esp. for incipient faults) Surveillance 9, Fès, May /15

4 1. Introduction State of the art State-of-the-art Vibration based fault/damage detection under non-measurable variable operating conditions (Sohn 2007, Deraemaeker et al. 2008, Surace and Worden 2010, Cross et al. 2012, Kullaa 2014, Figueiredo et al. 2014, Hios and Fassois 2014) Class 1: Features insensitive to uncertainty/operating condition factors (via PCA and so on) Class 2: The effects of uncertainty & operating conditions are treated via complete probabilistic models on the features Effects of uncertainty are independent from effects of faults (Vanlanduit et al. 2005) Baseline information should cover the uncertainty effects (Vamvoudakis et al. 2015) Main Drawback Hundreds or thousands of vibration signal records are usually needed for effective training of the method Surveillance 9, Fès, May /15

5 1. Introduction Present Study: Goal Measurable operating conditions in the inspection phase the GM based method (Hios and Fassois, 2014) may be employed Main Questions of this study Can the GM based method be extended to operate in case the operating condition is not measurable in the inspection phase? Can small incipient faults be also detected? Surveillance 9, Fès, May /15

6 2. The fault detection method 2. The fault detection method The main idea Model closest * to the implied current system in the min RSS sense ϵ? M? Yes is healthy No is faulty M? May by examined by checking the properties of M. As is a valid model, its residual sequence should be white. Hence, a whiteness test on the M residual sequence suffices Surveillance 9, Fès, May /15

7 2. Fault detection methodology Baseline phase Baseline phase Known Operating Conditions (k) The healthy dynamics under all operating conditions are represented by a global FP-ARX model (Fassois and Sakellariou, 2009). FP-ARX model Healthy Subspace p a a G ( k), bi b i, jgj ( k) i i, j j j 1 p j 1 AR X Residual a i,j, b i,j sequence : AR and X coefficients of projection G1(k) Gp(k) : functional basis Surveillance 9, Fès, May /15

8 3. Application to railway suspensions 3. Application to railway suspensions The railway vehicle Acceleration Measurement Points Faulty Components (fault scenarios) Fault scenarios Secondary and Primary suspension stiffness reduction (λ) (aging, air loss etc) λ = 5, 10, 20, 30, 40% Operating conditions Payload (k) increase (passengers, luggage, and so on) (nominal value: kg) k = 0 10% 1% = kg Surveillance 9, Fès, May /15

9 3. The application The experiments The Monte Carlo Experiments Sampling frequency Sampling bandwidth Signal length fs = 80 Hz 0-40 Hz N = samples (112s) Baseline phase Healthy system 11 experiments ONLY k=0-10% increase with a step of 1% Healthy system experiments (50 experiments per case) k = 0-10% increase with a step of 0.5% Inspection phase Unknown system Faulty system experiments (2 100 total) (10 per case) per fault scenario k=0-10% increase with a step of 0.5% λ = 5,10,20,30,40% Surveillance 9, Fès, May /15

10 3. The application Effects of the faults and variable operating condition The transmittance function is used: X[t]: Acceleration Point Y1 Y[t]: Acceleration Point Y2 Challenging fault detection Healthy/Faulty overlapping Surveillance 9, Fès, May /15

11 4. Fault detection performance assessment 4. Fault detection performance assessment Baseline Phase: FP-ARX identification M = 11 signal pairs FP-ARX(130,130)2 Functional subspace dimensionality: 2 Shifted Chebyshev Type II polynomials functions: G1(x) = 1, G2(x) = 4x-2 Indicative parameters trajectories Healthy Subspace Surveillance 9, Fès, May /15

12 4. Fault detection performance assessment First fault scenario First fault scenario Secondary suspension stiffness reduction * Conventional uncorrelatedness based method (Fassois and Sakellariou, 2009) based on a conventional ARX model of the dynamics Surveillance 9, Fès, May /15 *

13 4. Fault detection performance assessment Second fault scenario Second fault scenario Primary suspension stiffness reduction Surveillance 9, Fès, May /15

14 5. Concluding Remarks 5. Concluding Remarks A stochastic method for fault detection under non-measurable variable operating conditions was postulated: Unlike available methods, the postulated method requires only few signal pairs (experiments) in the baseline (learning) phase (presently only 11). Unlike an earlier method (Hios and Fassois 2014), the postulated one does not require measurement of the operating condition characteristics in the inspection phase. The method s effectiveness was demonstrated through an application study involving Monte Carlo experiments & also small (incipient) faults. Its advantages over a corresponding standard statistical time series method were also demonstrated. Surveillance 9, Fès, May /15

15 Thank you for your attention! Acknowledgement This research was supported by Grant (E.699) by the Research Committee of the University of Patras via the K. Karatheodori program. For more info please visit our site: Surveillance 9, Fès, May /15