Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

We present an analytical reduced-order model (macromodel) for an electrostatically actuated clamped circular plate. After establishing the equations governing the plate, we discretize the system by using a Galerkin approach. Accordingly, the distributed-parameter equations are reduced to a finite system of ordinary-differential equations in time. We solve the equations for the equilibrium states due to a general electric potential and determine the natural frequencies of the axisymmetric modes for the stable deflected position. Finally, we validate the model by using data from experiments performed on silicon-based microelectromechanical systems (MEMS). The reduced-order model accounts for residual stress, allows for general design variables, and is also robust up to the pull-in instability. Consequently, our macromodel is general and computationally strong enough to be an effective design tool.

We present an analytical reduced-order model (macromodel) for an electrically actuated clamped circular plate. After establishing the equations governing the plate, we discretize the system by using a Galerkin approach. The distributed-parameter equations are then reduced to a finite system of ordinary-differential equations in time. We solve these equations for the equilibrium states due to a general electric potential and determine the natural frequencies of the axisymmetric modes for the stable deflected position. Finally, we attempt to validate the model by using data from experiments performed on silicon-based microelectromechanical systems (MEMS). The reduced-order model accounts for both geometric nonlinear hardening and residual stress, allows for general design variables and is also robust up to the pull-in instability. Consequently, our macromodel is general and computationally strong enough to be an effective design tool.

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. The 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.

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of linear axes is typically a manual and time-consuming process. Manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. A method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios.

A wired sensor network was created to measure water-flow rate in a fire hose. An integrated electronic piezoelectric (IEPE) accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire hose. These sensors are small, lightweight, and they can be attached to the outside of the hose, not obstructing the water's flow path. A relationship was determined between the flow rate of the water and vibration detected by the accelerometer for a range of flow rates. The raw acceleration signal was used to calculate two metrics: the dominant frequency and the standard deviation of acceleration. In a future study, the relationship between the dominant-frequency metric and the flow rate will be applied to a wireless accelerometer network. The relationship will be used to determine the real-time fire hose flow rate critical for improving situational awareness on the fireground.

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a ‘sensor box’ to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

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