Abstract: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 co… Show more
“…For example, the National Institute of Standards and Technology (NIST) has been focused on developing methods, protocols, and tools to enable robust sensing, monitoring, diagnosis, prognosis, and control for PHM in manufacturing (National Institute of Standards and Technology 2015, 2016). A significant part of this research is the design and use of test beds to support the development of PHM across multiple control levels in a manufacturing system (Vogl et al 2015). The goal of these test beds is to generate:…”
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.
“…For example, the National Institute of Standards and Technology (NIST) has been focused on developing methods, protocols, and tools to enable robust sensing, monitoring, diagnosis, prognosis, and control for PHM in manufacturing (National Institute of Standards and Technology 2015, 2016). A significant part of this research is the design and use of test beds to support the development of PHM across multiple control levels in a manufacturing system (Vogl et al 2015). The goal of these test beds is to generate:…”
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.
“…Second, the matrix C and the new vector I are calculated with the operator 1, as shown in equation (5). If the element of I is a non-zone, then the corresponding rule has been triggered…”
Networks of five-axis machine tools produce huge amounts of process data. These data directly reflect the running condition of the machine tool but are seldom used to examine the machine performance. This study proposes a new data acquisition method based on the Object linking and embedding for Process Control protocol without any additional monitoring equipment. The data collection principle is explained, and a client is developed based on the SIEMENS 840D system. Considering less influence on the manufacturing process, a communication architecture for the machine network is designed with a special computer transmitting the data to the server. A compression algorithm is applied to reduce the storage capacity of massive amounts of data. Finally, a method for predicting the future performance of the machine tool is proposed using similarity analysis of the time series. A Petri net model is also established to diagnose possible failure causes. These methods significantly improve the machine tool reliability and find potentially important information from the data in the manufacturing process.
“…They implemented a data acquisition device in order to integrate machine-tools without connectivity in their solution. Vogl et al [15] proposed a multi-sensor system for machine tool axes monitoring and degradation assessment. A.A. Jaber [2] developed an embedded system for industrial robot condition monitoring using accelerometers at the flange of the robot.…”
This manuscript focuses on methodological and technological advances in the field of health assessment and predictive maintenance for industrial robots. We propose a non-intrusive methodology for industrial robot joint health assessment. Torque sensor data is used to create a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration. We prove the robustness and reliability of the methodology in a real industrial use case scenario. Then, an in depth mechanical inspection is carried out in order to identify the root cause of the failure diagnosed in this article. The proposed methodology is useful for medium and long term health assessment for industrial robots working in assembly lines, where years of almost uninterrupted work can cause irreversible damage.
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