Prognostics and health management (PHM) technologies reduce burdensome maintenance tasks of products or processes through diagnostic and prognostic activities. These activities provide actionable information that enable intelligent decision-making for improved performance, safety, reliability, and maintainability. However, standards for PHM system development, data collection and analysis techniques, data management, system training, and software interoperability appear to be partly lacking.

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... National Institute of Standards and Technology 1 (NIST) conducted a survey of PHM-related standards to determine the industries and needs addressed by such standards, the extent of these standards, and any similarities as well as potential gaps among the documents. Standards from various national and international organizations are summarized, including those from the Air Transport Association (ATA), the International Electrotechnical Commission (IEC), the International Organization for Standardization (ISO), the Society of Automotive Engineers (SAE), and the United States Army (US Army). Finally, recommendations are offered for the development of future PHM-related standards. PHM is dependent on data collection and processing for maintenance-related components or subsystems, so standards about data acquisition and processing are needed to influence the requirements for PHM systems development [1] . Standards for PHM are needed to address the lack of standardized terms, the lack of visibility, uniformity, and consistency of the PHM methods and tools, the need for compatibility and interoperability of PHM technology, and the needs for guidance in the practical use and development of PHM techniques [3]. Another goal of PHM is the comprehensive tracking of the performance and operational history of specific components; that is, components with serial numbers. The U.S. Army desires the capability of such a PHM system, but current obstacles include the lack of quality control, data management, software interoperability, and systematic serialization. These data-centric capabilities will enable the collection, transmission, storage, processing, and visibility of data within and among PHM systems [1]. Kalgren et al. [4] presented terminology and associated definitions for PHM that have been used for mechanical, structural, and propulsion technologies in an attempt to address this situation and to aid in the general application of PHM. For example, PHM was defined along with concepts like the gray-scale health index (from 0 to 1) for diagnostics and remaining useful life (RUL) for prognostics [4] . The creation of PHM systems is still difficult due to the inter-related tasks of design engineering, systems engineering, logistics, and user training [1]; no consistent methodology exists for assessing both the technical and economic benefits of PHM methods. Standardizing a specific set of data signal processing methods for PHM is perhaps ineffective, because each application requires diagnostic and prognostic techniques tailored to specific needs [1] . Roemer et al. [5] developed software for assessing the