Fourier transforms for vibration analysis: A review and case study

Wald, Randall; Khoshgoftaar, Taghi M.; Sloan, John C.

doi:10.1109/iri.2011.6009575

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Next, we removed each of their Transitions (Env), re-connecting their open places to places in the other nets according to Figure 5 and re-ran the verification. In 13 A classifier belongs to either a berth or the pool 14 The classifier in common between top and bottom ensembles is neither rewarded nor punished. all cases, we were able to (i) generate the full state space, (ii) prove a boundedness of 1 (i.e., 1-safety), and (iii) prove liveness (i.e., no dead markings).…”

Section: Verification Resultsmentioning

confidence: 99%

“…For each fault, each classifier occurrence can represent a distinct combination of classifier type [2], data preprocessing technique [13,14], and parameter settings. To date, over 100 combinations have been evaluated for vibration data.…”

Section: Ensemble Evolutionmentioning

confidence: 99%

“…Given over 100 classifiers, runtime assignment of a classifier to a berth must depend on the classifier's fitness, with the least fit swapped out for the most fit one in the pool 13 . For a fitness evaluation to be fair, it must consider the tradeoff between accuracy and timeliness.…”

Section: Ensemble Evolutionmentioning

confidence: 99%

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Ensemble Coordination for Discrete Event Control

Sloan

Khoshgoftaar

2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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An ensemble is a collection of independent processes, each tasked with drawing potentially differing conclusions about the same data. Using Petri nets, this paper formally describes how ensembles are organized and their behavior coordinated to effect distributed discrete event control of an ocean turbine prototype. Compositions, duals, reverses, and cliques formed over known Petri net graphs comprise the building blocks of the proposed ensemble coordination strategy. The behavior of an ensemble of controllers tasked with fault triage are subject to constraints formulated herein. The controller tasked with prognosis and health management (PHM) itself uses an ensemble of classifiers to detect faults. This ensemble is subject to constraints imposed by stream processing, which require a nonblocking form of rendezvous synchronization. Furthermore, results from each classifier must be fused in a manner that rewards that classifier's ability to predict faults. We identify two competing merit schemes -one based on individual classifier performance and the other on performance of the sub-ensembles to which that classifier participates. Finally, we model check these Petri nets and report their results.

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Section: Verification Resultsmentioning

confidence: 99%

Section: Ensemble Evolutionmentioning

confidence: 99%

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Ensemble Coordination for Discrete Event Control

Sloan

Khoshgoftaar

2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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“…Each of these must be dealt with appropriately, with details such as update intervals, urgency, and amount of history stored varying between them. Some sensors (such as vibration sensors) even require additional transformation before they can be properly interpreted by the software of the MCM system [69].…”

Section: Machine Condition Monitoring and Reliabilitymentioning

confidence: 99%

High Consequence Systems and Semantic Computing

Winter

Čukić

Khoshgoftaar

et al. 2013

Int. J. Semantic Computing

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Socioeconomic needs combined with technological advances are creating a demand for an increasing number of systems for which high-assurance is an essential attribute. These system designs, which increasingly include semantic computing components, span a broad spectrum of applications. They are incredibly diverse and their complexity is growing. The conditions under which these systems operate are such that system faults will occur and must be comprehensively accounted for in the systems' designs.This article investigates the landscape of high-assurance systems, the challenges these systems face, and what must be done to address those challenges. Challenges include societal needs, scienti¯c frontiers, dynamics of technological evolution, and drivers of current research models.gathering Google Chairman Eric Schmidt said``We need to set out an agenda for innovation for our country as a whole" [14].In the book Engines of Innovation it is argued that research universities must step up and play a more central role in tackling``the world's biggest problems" [63]. The core of such innovation often rests on the successful leveraging of computer and information technology within the context of a larger system. We postulate that, in the years to come, semantic computing will provide an essential fulcrum in many such systems. Independent of the underlying technologies employed, the implications of technological dependence are enormous: In their 2010 report, the President's Council of Advisors on Science and Technology (PCAST) stated that Networking and Information Technology (NIT)``underpins our national prosperity, health, and security". It should not be surprising then that the new millennium has seen a distinct shift of academic missions in the direction of interdisciplinary and applied use of computer technology in a shift which is often referred to as translational science.It is the context of leveraging computer technology to solve``the world's biggest problems" that has led innovators to envision and develop a wide range of highconsequence systems. A classical de¯nition of a high-consequence system is a system in which a high``cost" (or consequence) is associated with failure. Here the cost metric is not limited to the monetary axis, but can range across any axis of social value such as human life, or national security. From this de¯nition, it can be argued that the terms``world's biggest problems" and``high-consequence problems" are practically synonymous. The corollary to this argument is that one can then expect solutions to the world's biggest problems to be realized though innovative highconsequence systems. It is worth noting that these system designs are extremely diverse: they include autonomous decentralized control systems such as the system used to control Japan's bullet train, marine renewable energy systems such as those being developed o® the Florida coast, medical systems permitting the sharing of clinical guidelines and synthesis of clinical work°ows and protocols, supervisory control and acquisition (...

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“…There is no time-dependence and this technique presumes that the initial signal is stationary, with the relative importance of the frequencies not changing. It is not suitable for detecting incipient damage [9,10,11].…”

Section: ) Fft (Fast Fourier Transform)mentioning

confidence: 99%

Bearing fault diagnosis with an improved high frequency resonance technique

Segla

Wang

2012

IEEE 10th International Conference on Industrial Informatics

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Efficient detection of bearing failure is one of the most investigated engineering issues in several industries. The proposed method, based on High Frequency Resonance Technique (HFRT) allows detecting precise location of bearing's failure. The location of resonance area is computed by screening the signal's frequency spectrum. Some statistical features are adopted to get a preliminary inspection of the raw signal. This method is validated with Seeded Fault Test Data provided by Case Western Reserve University Bearing Data Center.

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Fourier transforms for vibration analysis: A review and case study

Cited by 9 publications

References 26 publications

Ensemble Coordination for Discrete Event Control

Ensemble Coordination for Discrete Event Control

High Consequence Systems and Semantic Computing

Bearing fault diagnosis with an improved high frequency resonance technique

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