An artificial neural network approach for remaining useful life prediction of equipments subject to condition monitoring
20 Jul 2009-pp 143-148
TL;DR: In this article, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring, which takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output.
Abstract: Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. An artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. Techniques are introduced to reduce the effects of the noise factors that are irrelevant to equipment degradation. The proposed method is validated using real-world vibration monitoring data.
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TL;DR: In this article, the authors developed an ANN approach utilizing both failure and suspension condition monitoring histories, which can be used for remaining useful life prediction of other equipments, and validated using vibration monitoring data collected from pump bearings in the field.
215 citations
Cites methods from "An artificial neural network approa..."
...Structure of the ANN model for remaining useful life prediction [17]...
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...The structure of the ANN model was proposed by Tian [17]....
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...The ANN model proposed by Tian [17] is used in the work....
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...Email address: tian@ciise.concordia.ca (Zhigang Tian). applications....
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...An extended version of the model was presented by Tian [17], which can deal with data that are not equally spaced and involve multiple measurement dimensions, and was applied to the RUL prediction of field bearings....
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TL;DR: The main prognostic idea of degradation pattern learning is proposed and illustrated, and an improved back propagation neural network is designed and analyzed as the implementation technique, in whose loss function an adjacent difference item is added.
175 citations
TL;DR: In this article, the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes, are reviewed.
Abstract: Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.
163 citations
TL;DR: The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction, and is evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics.
Abstract: Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics.
56 citations
TL;DR: A joint prognostic model (JPM) is proposed, where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensorData, and the observation epoch.
Abstract: With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion and RUL prediction methods are either not flexible enough to capture the highly nonlinear relationship between the health condition and the multisensor data or have not fully utilized the past observations to capture the degradation trajectory. In this article, we propose a joint prognostic model (JPM), where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensor data, and the observation epoch. A Bayesian updating scheme is developed to calculate the posterior distributions of the model parameters of each sensor data, which are further used to estimate the posterior predictive distributions of the residual life. The effectiveness and advantages of the proposed JPM are demonstrated using the commercial modular aero-propulsion system simulation data set.
26 citations
References
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TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
3,848 citations
"An artificial neural network approa..." refers background or methods in this paper
...Jardine et al developed the Proportional Hazards Model approach for CBM, where health condition indicators are predicted using the transition probability matrix [1, 3]....
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...Condition based maintenance (CBM) aims at achieving reliable and cost-effective operation of engineering systems such as aircraft systems, wind turbine generators, hydro power plants and manufacturing systems [1]....
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Book•
12 Jul 1996
TL;DR: The authors may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now.
Abstract: We may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.
2,278 citations
"An artificial neural network approa..." refers background in this paper
...Artificial neural networks (ANNs) have been considered to be very promising tools for equipment health condition and RUL prediction due to their adaptability, nonlinearity, and arbitrary function approximation ability [4]....
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Book•
29 Sep 2006TL;DR: The author examines the development of the Diagnostic Framework for Electrical/Electronic Systems and its applications in CBM/PHM systems, as well as some of the techniques used in model-Based Reasoning and other methods for Fault Diagnosis.
Abstract: PREFACE. ACKNOWLEDGMENTS. PROLOGUE. 1 INTRODUCTION. 1.1 Historical Perspective. 1.2 Diagnostic and Prognostic System Requirements. 1.3 Designing in Fault Diagnostic and Prognostic Systems. 1.4 Diagnostic and Prognostic Functional Layers. 1.5 Preface to Book Chapters. 1.6 References. 2 SYSTEMS APPROACH TO CBM/PHM. 2.1 Introduction. 2.2 Trade Studies. 2.3 Failure Modes and Effects Criticality Analysis (FMECA). 2.4 System CBM Test-Plan Design. 2.5 Performance Assessment. 2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies. 2.7 CBM/PHM in Control and Contingency Management. 2.8 References. 3 SENSORS AND SENSING STRATEGIES. 3.1 Introduction. 3.2 Sensors. 3.3 Sensor Placement. 3.4 Wireless Sensor Networks. 3.5 Smart Sensors. 3.6 References. 4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Preprocessing. 4.4 Signal Processing. 4.5 Vibration Monitoring and Data Analysis. 4.6 Real-Time Image Feature Extraction and Defect/Fault Classification. 4.7 The Virtual Sensor. 4.8 Fusion or Integration Technologies. 4.9 Usage-Pattern Tracking. 4.10 Database Management Methods. 4.11 References. 5 FAULT DIAGNOSIS. 5.1 Introduction. 5.2 The Diagnostic Framework. 5.3 Historical Data Diagnostic Methods. 5.4 Data-Driven Fault Classification and Decision Making. 5.5 Dynamic Systems Modeling. 5.6 Physical Model-Based Methods. 5.7 Model-Based Reasoning. 5.8 Case-Based Reasoning (CBR). 5.9 Other Methods for Fault Diagnosis. 5.10 A Diagnostic Framework for Electrical/Electronic Systems. 5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings. 5.12 References. 6 FAULT PROGNOSIS. 6.1 Introduction. 6.2 Model-Based Prognosis Techniques. 6.3 Probability-Based Prognosis Techniques. 6.4 Data-Driven Prediction Techniques. 6.5 Case Studies. 6.6 References. 7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS. 7.1 Introduction. 7.2 CBM/PHM Requirements Definition. 7.3 Feature-Evaluation Metrics. 7.4 Fault Diagnosis Performance Metrics. 7.5 Prognosis Performance Metrics. 7.6 Diagnosis and Prognosis Effectiveness Metrics. 7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems. 7.8 References. 8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION. 8.1 Introduction. 8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM. 8.3 Product Support without CBM. 8.4 Product Support with CBM. 8.5 Maintenance Scheduling Strategies. 8.6 A Simple Example. 8.7 References. APPENDIX. INDEX.
1,000 citations
"An artificial neural network approa..." refers methods in this paper
...The modelbased methods predict the remaining useful life using damage propagation models based on damage mechanics [2]....
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Book•
07 Nov 2002TL;DR: In this paper, the authors proposed a model for system reliability using Fault Tree Analysis (FTA) to evaluate the performance of one-and two-stage systems with different types of components.
Abstract: PrefaceAcknowledgments1 Introduction11 Needs for Reliability Modeling12 Optimal Design2 Reliability Mathematics21 Probability and Distributions211 Events and Boolean Algebra212 Probabilities of Events213 Random Variables and Their Characteristics214 Multivariate Distributions215 Special Discrete Distributions216 Special Continuous Distributions22 Reliability Concepts23 Commonly Used Lifetime Distributions24 Stochastic Processes241 General Definitions242 Homogeneous Poisson Process243 Nonhomogeneous Poisson Process244 Renewal Process245 Discrete-Time Markov Chains246 Continuous-Time Markov Chains25 Complex System Reliability Assessment Using Fault Tree Analysis3 Complexity Analysis31 Orders of Magnitude and Growth32 Evaluation of Summations33 Bounding Summations34 Recurrence Relations341 Expansion Method342 Guess-and-Prove Method343 Master Method35 Summary4 Fundamental System Reliability Models41 Reliability Block Diagram42 Structure Functions43 Coherent Systems44 Minimal Paths and Minimal Cuts45 Logic Functions46 Modules within a Coherent System47 Measures of Performance48 One-Component System49 Series System Model491 System Reliability Function and MTTF492 System Availability410 Parallel System Model4101 System Reliability Function and MTTF4102 System Availability of Parallel System with Two iid Components4103 System Availability of Parallel System with Two Different Components4104 Parallel Systems with n iid Components411 Parallel-Series System Model412 Series-Parallel System Model413 Standby System Model4131 Cold Standby Systems4132 Warm Standby Systems5 General Methods for System Reliability Evaluation51 Parallel and Series Reductions52 Pivotal Decomposition53 Generation of Minimal Paths and Minimal Cuts531 Connection Matrix532 Node Removal Method for Generation of Minimal Paths533 Generation of Minimal Cuts from Minimal Paths54 Inclusion-Exclusion Method55 Sum-of-Disjoint-Products Method56 Markov Chain Imbeddable Structures561 MIS Technique in Terms of System Failures562 MIS Technique in Terms of System Success57 Delta-Star and Star-Delta Transformations571 Star or Delta Structure with One Input Node and Two Output Nodes572 Delta Structure in Which Each Node May Be either an Input Node or an Output Node58 Bounds on System Reliability581 IE Method582 SDP Method583 Esary-Proschan (EP) Method584 Min-Max Bounds585 Modular Decompositions586 Notes6 General Methodology for System Design61 Redundancy in System Design62 Measures of Component Importance621 Structural Importance622 Reliability Importance623 Criticality Importance624 Relative Criticality63 Majorization and Its Application in Reliability631 Definition of Majorization632 Schur Functions633 L-Additive Functions64 Reliability Importance in Optimal Design65 Pairwise Rearrangement in Optimal Design66 Optimal Arrangement for Series and Parallel Systems67 Optimal Arrangement for Series-Parallel Systems68 Optimal Arrangement for Parallel-Series Systems69 Two-Stage Systems610 Summary7 Thek-out-of-n System Model71 System Reliability Evaluation711 The k-out-of-n:G System with iid Components712 The k-out-of-n:G System with Independent Components713 Bounds on System Reliability72 Relationship between k-out-of-n G and F Systems721 Equivalence between k-out-of-n:G and (n - k + 1)-out-of-n:F Systems722 Dual Relationship between k-out-of-n G and F Systems73 Nonrepairable k-out-of-n Systems731 Systems with iid Components732 Systems with Nonidentical Components733 Systems with Load-Sharing Components Following Exponential Lifetime Distributions734 Systems with Load-Sharing Components Following Arbitrary Lifetime Distributions735 Systems with Standby Components74 Repairable k-out-of-n Systems741 General Repairable System Model742 Systems with Active Redundant Components743 Systems with Load-Sharing Components744 Systems with both Active Redundant and Cold Standby Components75 Weighted k-out-of-n:G Systems8 Design of k-out-of-n Systems81 Properties of k-out-of-n Systems811 Component Reliability Importance812 Effects of Redundancy in k-out-of-n Systems82 Optimal Design of k-out-of-n Systems821 Optimal System Size n822 Simultaneous Determination of n and k823 Optimal Replacement Time83 Fault Coverage831 Deterministic Analysis832 Stochastic Analysis84 Common-Cause Failures841 Repairable System with Lethal Common-Cause Failures842 System Design Considering Lethal Common-Cause Failures843 Optimal Replacement Policy with Lethal Common-Cause Failures844 Nonlethal Common-Cause Failures85 Dual Failure Modes851 Optimal k or n Value to Maximize System Reliability852 Optimal k or n Value to Maximize System Profit853 Optimal k and n Values to Minimize System Cost86 Other Issues861 Selective Replacement Optimization862 TMR and NMR Structures863 Installation Time of Repaired Components864 Combinations of Factors865 Partial Ordering9 Consecutive-k-out-of-n Systems91 System Reliability Evaluation911 Systems with iid Components912 Systems with Independent Components92 Optimal System Design921 B-Importances of Components922 Invariant Optimal Design923 Variant Optimal Design93 Consecutive-k-out-of-n:G Systems931 System Reliability Evaluation932 Component Reliability Importance933 Invariant Optimal Design934 Variant Optimal Design94 System Lifetime Distribution941 Systems with iid Components942 System with Exchangeable Dependent Components943 System with (k - 1)-Step Markov-Dependent Components944 Repairable Consecutive-k-out-of-n Systems95 Summary10 Multidimensional Consecutive-k-out-of-n Systems101 System Reliability Evaluation1011 Special Multidimensional Systems1012 General Two-Dimensional Systems1013 Bounds and Approximations102 System Logic Functions103 Optimal System Design104 Summary11 Other k-out-of-n and Consecutive-k-out-of-n Models111 The s-Stage k-out-of-n Systems112 Redundant Consecutive-k-out-of-n Systems113 Linear and Circular m-Consecutive-k-out-of-n Model114 The k-within-Consecutive-m-out-of-n Systems1141 Systems with iid Components1142 Systems with Independent Components1143 The k-within-(r, s)/(m, n):F Systems115 Series Consecutive-k-out-of-n Systems116 Combined k-out-of-n:F and Consecutive-kc-out-of-n:F System117 Combined k-out-of-mn:F and Linear (r, s)/(m, n):F System118 Combined k-out-of-mn:F, One-Dimensional Con/kc/n:F, and Two-Dimensional Linear (r, s)/(m, n):F Model119 Application of Combined k-out-of-n and Consecutive-k-out-of-n Systems1110 Consecutively Connected Systems1111 Weighted Consecutive-k-out-of-n Systems11111 Weighted Linear Consecutive-k-out-of-n:F Systems11112 Weighted Circular Consecutive-k-out-of-n:F Systems12 Multistate System Models121 Consecutively Connected Systems with Binary System State and Multistate Components1211 Linear Multistate Consecutively Connected Systems1212 Circular Multistate Consecutively Connected Systems1213 Tree-Structured Consecutively Connected Systems122 Two-Way Consecutively Connected Systems123 Key Concepts in Multistate Reliability Theory124 Special Multistate Systems and Their Performance Evaluation1241 Simple Multistate k-out-of-n:G Model1242 Generalized Multistate k-out-of-n:G Model1243 Generalized Multistate Consecutive-k-out-of-n:F System125 General Multistate Systems and Their Performance Evaluation126 SummaryAppendix: Laplace TransformReferencesBibliographyIndex
678 citations
"An artificial neural network approa..." refers background in this paper
...Weibull distribution is very powerful in representing various practical lifetime distributions, and flexible enough to represent distributions with different scales and shapes [8]....
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TL;DR: Performance Assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns.
577 citations
"An artificial neural network approa..." refers methods in this paper
...Lee et al [5] proposed to extract an overall health indicator based on the collected condition data, and predict future health indicator values using the autoregressive moving average (ARMA) method and Elman neural networks....
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