FAILURE PROGNOSIS USING NONLINEAR SHORT-TIME PREDICTION AND MULTI-TIME SCALE RECURSIVE ESTIMATION

In this paper we describe a new, general purpose machin- ery diagnostic/prognostic algorithm for tracking and predicting evolving damage using only available "macroscopic" observable quantities. The damage is viewed as occurring in a hierarchi- cal dynamical system consisting of a directly observable, "fast" subsystem coupled with a hidden, "slow" subsystem describing damage evolution. This method provides damage diagnostics and failure prognostics requiring only the measurements from the fast subsystem and a model of the slow subsystem. Damage tracking is accomplished by a two-time-scale modeling strategy based on phase space reconstruction using the measured fast- time data. Short-time predictive models are constructed using the reconstructed phase space of the reference (undamaged) fast sub- system. Later, fast-time data for the damaged system is collected and used to estimate the short-time reference model prediction error, or a tracking function. An average value of the tracking function over a given data record is used as a tracking metric, or measure of the current damage state. Recursive, nonlinear filter- ing is used to estimate the actual damage state based on the track- ing metric input. Estimates of remaining useful life are obtained recursively using a linear Kalman filter. This method is applied to an experimental nonlinear oscillator containing a beam with a crack which propagates to complete failure. We demonstrate the ability to track the evolving damage state of the beam using only strain time series data. We also give accurate predictions of remaining useful life, in real time, beginning well in advance of

[1]  N. R. Iyer,et al.  DETECTION OF ROLLER BEARING DEFECTS USING EXPERT SYSTEM AND FUZZY LOGIC , 1996 .

[2]  Jian Chen,et al.  THE APPLICATION OF CORRELATION DIMENSION IN GEARBOX CONDITION MONITORING , 1999 .

[3]  Steven Y. Liang,et al.  STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS , 2000 .

[4]  C. James Li,et al.  Non-linear arx model-based kullback index for fault detection of a screw compressor , 1995 .

[5]  Charles R. Farrar,et al.  Application of the strain energy damage detection method to plate-like structures , 1999 .

[6]  Richard David Neilson,et al.  THE USE OF CORRELATION DIMENSION IN CONDITION MONITORING OF SYSTEMS WITH CLEARANCE , 2000 .

[7]  P. D. McFadden,et al.  A Signal Processing Technique for Detecting Local Defects in a Gear from the Signal Average of the Vibration , 1985 .

[8]  Philip Holmes,et al.  A magnetoelastic strange attractor , 1979 .

[9]  Keith Worden,et al.  TIME–FREQUENCY ANALYSIS IN GEARBOX FAULT DETECTION USING THE WIGNER–VILLE DISTRIBUTION AND PATTERN RECOGNITION , 1997 .

[10]  David C. Swanson,et al.  PROGNOSTIC MODELLING OF CRACK GROWTH IN A TENSIONED STEEL BAND , 2000 .

[11]  Cecilia Surace,et al.  DAMAGE ASSESSMENT OF MULTIPLE CRACKED BEAMS: NUMERICAL RESULTS AND EXPERIMENTAL VALIDATION , 1997 .

[12]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[13]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[14]  Rolf Isermann,et al.  Process Fault Detection Based on Modeling and Estimation Methods , 1982 .

[15]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[16]  Xiao Li,et al.  Study and performance evaluation of some nonlinear diagnostic methods for large rotating machinery , 1993 .

[17]  P. D. McFadden,et al.  APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION , 1996 .

[18]  Lanto Rasolofondraibe,et al.  Fault detection and monitoring of a ball bearing benchtest and a production machine via autoregressive spectrum analysis , 1998 .

[19]  S. Li,et al.  FUZZY CLUSTERING FOR AUTOMATED TOOL CONDITION MONITORING IN MACHINING , 1996 .

[20]  Gaoyong Luo,et al.  Real-Time Condition Monitoring by Significant and Natural Frequencies Analysis of Vibration Signal with Wavelet Filter and Autocorrelation Enhancement , 2000 .

[21]  Yimin Fan,et al.  Recurrent Neural Networks for Fault Diagnosis and Severity Assessment of a Screw Compressor , 1999 .

[22]  Keith Worden,et al.  STRUCTURAL FAULT DETECTION USING A NOVELTY MEASURE , 1997 .

[23]  Shih-Fu Ling,et al.  Machinery Diagnosis Based on Wavelet Packets , 1997 .

[24]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[25]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS , 1996 .

[26]  Keith Worden,et al.  DAMAGE DETECTION USING OUTLIER ANALYSIS , 2000 .

[27]  P. F. Pai,et al.  LOCATING STRUCTURAL DAMAGE BY DETECTING BOUNDARY EFFECTS , 2000 .

[28]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[29]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[30]  Lei Tian,et al.  Damage detection in smart structures through sensitivity-enhancing feedback control , 1999, Smart Structures.

[31]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[32]  P. D. McFadden,et al.  Early Detection of Gear Failure by Vibration Analysis--I. Calculation of the Time Frequency Distribution , 1993 .

[33]  Brian F. Feeny,et al.  PARAMETRIC IDENTIFICATION OF AN EXPERIMENTAL MAGNETO-ELASTIC OSCILLATOR , 2001 .

[34]  A. N. Sharkovskiĭ.  Dynamic systems and turbulence , 1989 .

[35]  R. Ruotolo,et al.  USING SVD TO DETECT DAMAGE IN STRUCTURES WITH DIFFERENT OPERATIONAL CONDITIONS , 1999 .

[36]  E. Ott.  Chaos in Dynamical Systems: Contents , 1993 .

[37]  E. J. Williams,et al.  STRUCTURAL DAMAGE DETECTION BY A SENSITIVITY AND STATISTICAL-BASED METHOD , 1998 .

[38]  G. De Roeck,et al.  Damage detection in bridges using modal curvatures: application to a real damage scenario , 1999 .

[39]  Grant P. Steven,et al.  VIBRATION-BASED MODEL-DEPENDENT DAMAGE (DELAMINATION) IDENTIFICATION AND HEALTH MONITORING FOR COMPOSITE STRUCTURES — A REVIEW , 2000 .

[40]  W. J. Wang,et al.  Application of orthogonal wavelets to early gear damage detection , 1995 .

[41]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice , 1993 .

[42]  Chris K. Mechefske,et al.  OBJECTIVE MACHINERY FAULT DIAGNOSIS USING FUZZY LOGIC , 1998 .

[43]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—II. SELECTION OF EXPERIMENTAL PARAMETERS , 1996 .

[44]  Paul R. White,et al.  BISPECTRAL ANALYSIS OF THE BILINEAR OSCILLATOR WITH APPLICATION TO THE DETECTION OF FATIGUE CRACKS , 1998 .

[45]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[46]  Thomas Parisini,et al.  Model-based fault diagnosis using nonlinear estimators: a neural approach , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[47]  Steven Y. Liang,et al.  Adaptive Prognostics for Rolling Element Bearing Condition , 1999 .

[48]  Joseph McGhee,et al.  Neural networks applied for the identification and fault diagnosis of process valves and actuators , 1997 .

[49]  David Chelidze.  A nonlinear observer for damage evolution tracking , 2000 .

[50]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[51]  Nuno M. M. Maia,et al.  DAMAGE DETECTION USING THE FREQUENCY-RESPONSE-FUNCTION CURVATURE METHOD , 1999 .

[52]  Jaycee H. Chung,et al.  DETECTING STRUCTURAL DAMAGE USING FREQUENCY RESPONSE FUNCTIONS , 1998 .

[53]  P. Blau,et al.  Detection, Diagnosis, and Prognosis of Rotating Machinery to Improve Reliability, Maintainability, and Readiness Through the Application of New and Innovative Techniques , 1991 .