Assessment of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation.

Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Without careful planning, maintenance can be very costly. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process and part quality data, and equipment and process fault and failure data. These data streams can be extremely informative, yet the massive volume and complexity of this data can be overwhelming, confusing, and sometimes paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer, or add more intelligence to assessing robot workcell health. This article presents the most recent effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor's performance during verification testing. Lessons learned indicate that the test process can be an effective means of quantifying the sensor's measurement capability particularly after test process anomalies are addressed in future efforts.

Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations - Summary of an Interactive Workshop Highlighting PHM Trends.

Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community's priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented.

A review of diagnostic and prognostic capabilities and best practices for manufacturing.

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.

Developing a hierarchical decomposition methodology to increase manufacturing process and equipment health awareness.

Manufacturing systems are becoming increasingly complex as more advanced and emerging technologies are integrated into the factory floor to yield new processes or increase the efficiency of existing processes. As greater complexity is formed across the factory, new relationships are often generated that can lead to advanced capabilities, yet produce unforeseen faults and failures. Industrial robot arm work cells within the manufacturing environment present increasing complexity, emergent technologies, new relationships, and unpredicted faults/failures. To maintain required levels of productivity, process quality, and asset availability, manufacturers must reconcile this complexity to understand how the health degradation of constituent physical elements and functional tasks impact one another through the monitoring of critical informative measures and metrics. This article presents the initial efforts in developing a novel hierarchical decomposition methodology. The innovation in this method is that it provides the manufacturer with sufficient discretion to physically deconstruct their system and functionally decompose their process to user-defined levels based upon desired monitoring, maintenance, and control levels. This enables the manufacturer to specify relationships within and across the physical, functional, and information domains to identify impactful health degradations without having to know all possible failure modes. The hierarchical decomposition methodology will advance the state of the art in terms of improving machine health by highlighting how health degradations propagate through the relationship network prior to a piece of equipment compromising the productivity or quality of a process. The first two steps of the methodology, physical decomposition and functional decomposition, are defined in detail and applied to a multi-robot work cell use case.

Quick health assessment for industrial robot health degradation and the supporting advanced sensing development.

Robotic technologies are becoming more integrated with complex manufacturing environments. The addition of greater complexity leads to more sources of faults and failures that impact a robot system's reliability. Industrial robot health degradation needs to be assessed and monitored to minimize unexpected shutdowns, improve maintenance techniques, and optimize control strategies. A quick health assessment methodology is developed at the U.S. National Institute of Standards and Technology (NIST) to quickly assess a robot's tool center position and orientation accuracy degradation. An advanced sensing development approach to support the quick health assessment methodology is also presented in this paper. The advanced sensing development approach includes a seven-dimensional (7-D) measurement instrument (time, X, Y, Z, roll, pitch, and yaw) and a smart target to facilitate the quick measurement of a robot's tool center accuracy.

Hierarchical Modeling of a Manufacturing Work Cell to Promote Contextualized PHM Information Across Multiple Levels.

With the ever-increasing demand for reconfigurability and modularity in manufacturing, industrial work cells are increasingly integrating newer and more diverse technologies to not only support the production of a wider range of parts, but also ease the repair or replacement of faulty systems and components. Complex relationships between different elements of a work cell originate from the integration of multiple layers of hardware and software needed to successfully execute the complicated manufacturing processes. Much work within the science of PHM (prognostics and health management) has been dedicated towards the management of some of this complexity via monitoring, diagnostic, and prognostic technologies. The strategic application of PHM technologies has been shown to effectively reduce equipment/process downtime and lower maintenance costs. Part of the challenge of PHM, particularly for manufacturers, is to know exactly where, and how to apply PHM within their work cell operations to gain the maximum actionable information. This problem is further compounded for small to medium-sized manufacturers (SMMs) who are typically limited in their resources and investment capital. Effectively designing and implementing PHM requires a fundamental understanding of the overall work cell and its constituent physical components and sub-components. Likewise, understanding the relationships between these physical elements and how these elements relate to one another is critical to determining how the degradation of one element will impact the degradation of another. The National Institute of Standards and Technology (NIST) is researching various PHM technologies that aim to enhance decision-making at the factory floor to promote smarter maintenance and control strategies. Part of NIST's research focuses on the decomposition of a work cell into a hierarchical structure to understand the physical and functional relationships among the overall system's critical elements. This physical and functional decomposition is a necessity to promote a meaningful rollup of diagnostic and prognostic information from the lower levels to the higher levels of the hierarchy. The hierarchy seeks to encapsulate how the overall system, and its subsequent components, will behave when an element within the system is compromised or begins to fail. Neighboring components and sub-components could be subject to the 'domino effect' or the 'ripple effect', making diagnosing the root cause of a cascade alarms difficult without some reflective model of the system. This paper presents NIST's efforts to develop a hierarchical decomposition methodology that will support PHM design and implementation within a complex work cell.

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics.

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing.

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

The present status and future growth of maintenance in US manufacturing: results from a pilot survey.

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.

Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems.

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decision-making in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell, and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes.

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.

Adaptive Multi-scale PHM for Robotic Assembly Processes.

Adaptive multiscale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. As a rule, PHM information is not used in high-level decision-making in manufacturing systems. AM-PHM leverages and integrates component-level PHM information with hierarchical relationships across the component, machine, work cell, and production line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. A description of the AM-PHM methodology with a simulated canonical robotic assembly process is presented.

Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop.

The National Institute of Standards and Technology (NIST) hosted the Roadmapping Workshop - Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) in Fall 2014 to discuss the needs and priorities of stakeholders in the PHM4SMS technology area. The workshop brought together over 70 members of the PHM community. The attendees included representatives from small, medium, and large manufacturers; technology developers and integrators; academic researchers; government organizations; trade associations; and standards bodies. The attendees discussed the current and anticipated measurement science challenges to advance PHM methods and techniques for smart manufacturing systems; the associated research and development needed to implement condition monitoring, diagnostic, and prognostic technologies within manufacturing environments; and the priorities to meet the needs of PHM in manufacturing. This paper will summarize the key findings of this workshop, and present some of the critical measurement science challenges and corresponding roadmaps, i.e., suggested courses of action, to advance PHM for manufacturing. Milestones and targeted capabilities will be presented for each roadmap across three areas: PHM Manufacturing Process Techniques; PHM Performance Assessment; and PHM Infrastructure - Hardware, Software, and Integration. An analysis of these roadmaps and crosscutting themes seen across the breakout sessions is also discussed.

Evaluation methodology and metrics employed to assess the TRANSTAC two-way, speech-to-speech translation systems.

One of the most difficult challenges that military personnel face when operating in foreign countries is clear and successful communication with the local population. To address this issue, the Defense Advanced Research Projects Agency (DARPA) is funding academic institutions and industrial organizations through the Spoken Language Communication and Translation System for Tactical Use (TRANSTAC) program to develop practical machine translation systems. The goal of the TRANSTAC program is to demonstrate capabilities to rapidly develop and field free-form, two-way, speech-to-speech translation systems that enable speakers of different languages to communicate with one another in real-world tactical situations without an interpreter. Evaluations of these technologies are a significant part of the program and DARPA has asked the National Institute of Standards and Technology (NIST) to lead this effort. This article presents the experimental design of the TRANSTAC evaluations and the metrics, both quantitative and qualitative, that were used to comprehensively assess the systems' performance.