Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

A review on machinery diagnostics and prognostics implementing condition-based maintenance

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Remaining useful life estimation - A review on the statistical data driven approaches

A review on empirical mode decomposition in fault diagnosis of rotating machinery

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

Optimal Reliability Modeling: Principles and Applications

Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.

Ming J. Zuo

Papers

A review on empirical mode decomposition in fault diagnosis of rotating machinery

Optimal Reliability Modeling: Principles and Applications

Current status of machine prognostics in condition-based maintenance: a review

Condition monitoring and fault diagnosis of planetary gearboxes: A review

Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection