A testbed for data fusion for helicopter diagnostics and prognostics

Brotherton, T.; Grabill, P.; Friend, Richard H.; Sotomayer, B.; Berry, John D.

doi:10.1109/aero.2003.1234178

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…22 New CI data is compared to the standard dataset to determine if the new data fits within the standard data set as normal or a specific fault class. Then data is accepted or rejected based on a rejection threshold.…”

Section: Hums Survey Resultsmentioning

confidence: 99%

Investigation of Current Methods to Identify Helicopter Gear Health

Dempsey

Lewicki

2007

2007 IEEE Aerospace Conference

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This paper provides an overview of current vibration methods used to identify the health of helicopter transmission gears. The gears are critical to the transmission system that provides propulsion, lift and maneuvering of the helicopter. This paper reviews techniques used to process vibration data to calculate conditions indicators (CI's), guidelines used by the government aviation authorities in developing and certifying the Health and Usage Monitoring System (HUMS), condition and health indicators used in commercial HUMS, and different methods used to set thresholds to detect damage. Initial assessment of a method to set thresholds for vibration based condition indicators applied to flight and test rig data by evaluating differences in distributions between comparable transmissions are also discussed. Gear condition indicator FM4 values are compared on an OH58 helicopter during 14 maneuvers and an OH58 transmission test stand during crack propagation tests. Preliminary results show the distributions between healthy helicopter and rig data are comparable and distributions between healthy and damaged gears show significant differences.

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Section: Hums Survey Resultsmentioning

confidence: 99%

Investigation of Current Methods to Identify Helicopter Gear Health

Dempsey

Lewicki

2007

2007 IEEE Aerospace Conference

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“…MacConnell (2007MacConnell ( , 2008) ISHM (Integrated Systems Health Management). Brotherton et al (2002Brotherton et al ( , 2003 identified one significant challenge in PHM: the low "Signal/Noise" ratio, a term they used analogically in fault detection. In other words, fault distribution is strongly skewed and the data in the long "tail" is hard to collect.…”

Section: A Brief Literature Reviewmentioning

confidence: 98%

A new life system approach to the Prognostic and Health Management (PHM) with survival analysis, dynamic hybrid fault models, evolutionary game theory, and three-layer survivability analysis

2009

2009 IEEE Aerospace Conference

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In this paper, I propose a new architecture for PHM, which is characterized by life-system approachtreating PHM as a hierarchical system with fundamental properties similar to those of life systems. Conceptually, besides drawing on the important concepts from existing PHM theory and practice such as life cycle, condition-based maintenance (CBM), remaining useful lifetime (RUL), I draw on the dynamic hybrid fault models (DHF) from fault tolerance theory and agreement algorithms, three-layer survivability analysis from survivable network systems (SNS), population dynamics from population ecology, and survival analysis from biostatistics and biomedicine. Methodologically, three main mathematical tools: survival analysis (including competing risks analysis and multivariate survival analysis), dynamic hybrid fault models and evolutionary game theory (EGT) are applied for PHM modeling and analysis. Operationally, the three-layer survivability analysis is applied to deal with the so-called UUUR(Unpredictable, latent, Unobserved or Unobservable Risks) events and to achieve sound decision-making. Overall, the advantages of the new architecture include: (1) Offer a flexible architecture that is not only compatible with existing components/approaches of PHM, such as lifetime, reliability, maintainability, safety, data-driven prognostics, and model-based prognostics, but also readily extendable to incorporate fault tolerance, survivability, and security. (2) Utilize survival analysis, competing risks analysis, and multivariate survival analysis for better modeling of lifetime and reliability at both individual and population (group of components) levels.(3) Approach the fault tolerance and reliability of the monitoring sensor network in PHM and those of the underlying physical system with the DHF models. (4) Analyze the system survivability (sustainability) with the three-layer survivability analysis approaches. (5) Capture security events with UUUR events and incorporate security policies into PHM.

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“…Apart from some testbeds used for benchmarking PHM solutions targeting military applications, where there is a reference to requirements for the final system only, but not for the testbed design, (Brotherton et al, 2003;Delaney et al, 2009), the use of standards for the design process of such testbeds and for achieving data-integrity is not mentioned in the reviewed literature.…”

Section: Diagnostics and Prognostics Testbeds -A Literature Reviewmentioning

confidence: 99%

Requirements and Data Integrity Considerations for Diagnostics Testbeds

Bardakis¹,

Niculita

Wallace

2021

IJPHM

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The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.

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A testbed for data fusion for helicopter diagnostics and prognostics

Cited by 8 publications

References 4 publications

Investigation of Current Methods to Identify Helicopter Gear Health

Investigation of Current Methods to Identify Helicopter Gear Health

A new life system approach to the Prognostic and Health Management (PHM) with survival analysis, dynamic hybrid fault models, evolutionary game theory, and three-layer survivability analysis

Requirements and Data Integrity Considerations for Diagnostics Testbeds

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