Industrial Robot Accuracy Degradation Monitoring and Quick Health Assessment

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Production Efficiency ◽  
Health Management ◽  
Research Effort ◽  
Industrial Robot ◽  
Health Assessment ◽  
Test Method ◽  
Health State ◽  
Future Health ◽  
Robot Accuracy ◽  
Degradation Monitoring

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

Accuracy Degradation Analysis for Industrial Robot Systems

2017 ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Production Efficiency ◽  
Health Management ◽  
Industrial Robot ◽  
Health Assessment ◽  
Test Method ◽  
Fiber Placement ◽  
Accuracy Requirement ◽  
Measurement Science ◽  
Robot Systems ◽  
Automated Fiber Placement

As robot systems become increasingly prevalent in manufacturing environments, the need for improved accuracy continues to grow. Recent accuracy improvements have greatly enhanced automotive and aerospace manufacturing capabilities, including high-precision assembly, two-sided drilling and fastening, material removal, automated fiber placement, and in-process inspection. The accuracy requirement of those applications is primarily a function of two main criteria: (1) The pose accuracy (position and orientation accuracy) of a robot system’s tool center position (TCP), and (2) the ability of a robot system’s TCP to remain in position or on-path when loads are applied. The degradation of a robot system’s tool center accuracy can lead to a decrease in manufacturing quality and production efficiency. Given the high output rate of production lines, it is critical to develop technologies to verify and validate robot systems’ health assessment techniques, particularly the accuracy degradation. In this paper, the National Institute of Standards and Technology’s (NIST) effort to develop the measurement science to support the monitoring, diagnostics, and prognostics (collectively known as prognostics and health management (PHM)) of robot accuracy degradation is presented. This discussion includes the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), and algorithms to analyze the data.

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

2019 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi
Keyword(s):  
Gas Turbine ◽  
Field Data ◽  
Health Management ◽  
Theoretical Background ◽  
Health State ◽  
Prediction Errors ◽  
Probabilistic Prediction ◽  
Future Health ◽  
Bayesian Hierarchical ◽  
Prediction Reliability

Abstract The prediction of the time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future health state thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM predicted outputs. Then, BHM is applied to both simulated and field data representative of gas turbine degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors were found to be lower than 1.0 % or 1.7 % for single- or multi-step prediction, respectively.

Gas Turbine Accessory Health Management Evaluation Using a Hybrid Prognostic Approach

2008 ◽  
Author(s):  
Carl S. Byington ◽  
Matthew J. Watson ◽  
Sudarshan P. Bharadwaj
Keyword(s):  
Gas Turbine ◽  
Health Management ◽  
Health Assessment ◽  
Physical Models ◽  
Data Driven ◽  
Health State ◽  
Time To Failure ◽  
Detection Rates ◽  
Hybrid Techniques ◽  
Mission Success

The authors have developed model-based and data-driven techniques aimed at providing a more reliable health assessment of gas turbine engine accessory components, which have contributed to a significant number of events that compromise mission success and equipment availability in military aircraft. As part of this approach, a physical model is used to derive parameters indicative of component-specific faults. Statistical fault classifiers and evolutionary prognostics methods are then used to track these parameters and identify the most likely health state and time to failure for each component. This assessment is fused with the results of independent data-driven routines, which are also used to analyze dynamic signal response and detect faults that would be difficult to incorporate into physical models. The developed approach was demonstrated using an experimental setup representative of aircraft fuel and lubrication systems. Pump leakage, pump gear damage, and valve blockage were seeded on the setup, and the developed routines were trained with high-bandwidth experimental data. The approach produced wide separation between baseline and faulted cases, yielding negligible missed detection rates for moderate faults and reasonable missed detection rates for an incipient valve blockage fault. The demonstration produced a quantifiable estimate of achievable performance using the hybrid techniques.

scholarly journals Modelling Past and Future Urban Ecosystem Health Assessment —A Case Study of Kunming

2021 ◽  
Author(s):  
Hui Li ◽  
Yang Liu ◽  
Weiguo Sheng ◽  
Huiyi Qiu ◽  
Yilu Zhou ◽  
...  
Keyword(s):  
Health Assessment ◽  
Urban Ecosystem ◽  
Assessment Model ◽  
Capital City ◽  
Health State ◽  
Indicator Values ◽  
Grey System ◽  
Future Health ◽  
Human Environment

Abstract Rapid urbanisation leads to increasing conflict in the human-environment relationship. The health of urban ecosystems is deteriorating and this will directly harmcommunity health and wellbeing. This paper used Kunming, the capital city of Yunnan Province, China as a case study. A health assessment model for the urban ecosystem of Kunming was built using 25 indicators reflecting five measures: driving force, pressure, state, impact and response. We calculated the indicator values in 2006, 2011 and 2016 with remote sensing and statistical data. We used the entire-array-polygon method to draw polygon graphs and calculate the overall indicator values of the three periods, based on the standardised values of all indicators. All the indicator values were below 0.25, showing that the urban ecosystem was assessed as unhealthy. On the basis of the past health assessment model, we applied a grey system forecasting method to predict the future health of the urban ecosystem. If the current trends continued, the urban ecosystem would remain in an unhealthy state for 5–10 years. Strong measures should be implemented to improve the overall health of the urban ecosystem. This paper serves as an early warning of the health state of the urban ecosystem in Kunming.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Qinming Liu ◽  
Ming Dong
Keyword(s):  
Monte Carlo ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Markov Model ◽  
Sequential Monte Carlo ◽  
Health Management ◽  
Health State ◽  
Health States ◽  
Complex Nonlinear System ◽  
Complex Nonlinear Systems

Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.

Assessment of Current Health of Hard Disk Drives

2010 ◽  
Author(s):  
Abe Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Health Management ◽  
State Of The Art ◽  
Hard Disk Drives ◽  
Health Assessment ◽  
Hard Disk ◽  
Assessment Method ◽  
Hard Disks ◽  
Disk Drives ◽  
Future Directions ◽  
Self Monitoring

Prognostics and health management of computer hard disk drives is beneficial from two different angles: it can help computer users plan for timely replacement of HDDs before they catastrophically fail and cause serious data loss; it can also help product recover facilities reuse hard disks recovered from the end-of-life computers for building refurbished computers. This paper presents a HDD health assessment method using Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. It also presents the state-of-the art results in monitoring the condition of hard disks and offers future directions for distributed hard disk monitoring.

Youngstock health: Effective disease prevention today ensuring tomorrow's profitable herd

Livestock ◽  
2020 ◽  
Vol 25 (Sup2) ◽  
pp. 1-24
Author(s):  
David Barrett ◽  
Oliver Tilling ◽  
Ellie Button ◽  
Kat Hart ◽  
Fiona MacGillivray ◽  
...  
Keyword(s):  
Disease Prevention ◽  
Production Efficiency ◽  
Health Management ◽  
Antibiotic Use ◽  
Lung Damage ◽  
Poor Growth ◽  
Dairy Heifers ◽  
New Science ◽  
Bull Calves ◽  
Dairy Bull

Foreword Proactive youngstock health management is critical not only to optimise animal welfare and production efficiency and profitability, but also to minimising the environmental impact of livestock production. The morbidity and mortality rates tolerated by some producers, and at times even accepted by their vets, are often far too high. Whether it is the loss of dairy bull calves, who may have little monetary value but nevertheless contribute to both the carbon footprint and other environmental impacts of a dairy if not utilised for food production, dairy heifers with the additional loss of the best genetics in the herd or a beef suckler calf representing the only product of the cow that year, we need to do all we can to prevent death and disease. Even where calves do not die, managing sick animals is costly in treatment and labour and antibiotic use in these animals to treat, and on some farms still to prevent disease, is very likely to contribute to antimicrobial resistance. The only logical conclusion that one can come to is that if cattle units are to remain viable and produce sustainable milk and meat, we need to redouble efforts to prevent disease. Sometimes we talk of new science, while at other times we find ourselves repeating ‘the same old message’. The need for adequate colostrum management is one such case, the messages may not be new but far too many calves still fail to receive enough maternally derived antibody, making the article in this supplement on colostrum vital reading. Don't assume you, or more importantly your clients, know everything there is to know about colostrum. After ensuring calves have received initial protection via maternal derived antibody from colostrum then we need to ensure they are protected from infections as they grow, particularly respiratory diseases. Despite having had good vaccines for over 20 years, their uptake in the national herd I believe is still suboptimal, far too many calves still receive antimicrobials, and poor growth rates are common due to chronic lung damage. Ellie Button explains well in her article ‘Calf disease: an immunological perspective’ the calf's innate and acquired immunity and describes how an understanding of the calf's developing immunity can be used to enhance disease prevention. Finally, Kat Hart and colleagues discuss communication and promoting behaviour change in ‘How to engage farmers in youngstock care: a clinical forum’, something that in the past we have often forgotten. It's not good enough for vets to understand the science and turn a blind eye to poor on farm practices, or to simply tell clients what to do and walk away expecting them to do as they have been told! We often need to motivate clients towards real lasting change, and to do that we all need to communicate better. Together the three articles in this supplement are a powerful combination, the challenge is to read them and then effect valuable changes on your clients’ farms.

Health Assessment Based on Fuzzy Synthetic Evaluation for Civil Aircrafts

2011 ◽  
Vol 335-336 ◽  
pp. 985-988
Author(s):  
Bao Hui Jia ◽  
Ze Dong Sun
Keyword(s):  
Health Status ◽  
Health Management ◽  
Health Assessment ◽  
Fuzzy Synthetic Evaluation ◽  
Hydraulic Pump ◽  
Quantitative Level ◽  
Key Technologies ◽  
Civil Aircraft ◽  
Evaluation Algorithm ◽  
Synthetic Evaluation

Health assessment is one of the key technologies for civil aircraft health management system. In order to access the health status of components, subsystems and systems of civil aircrafts, this paper explicitly defines the health status, and presents the fuzzy synthetic evaluation algorithm. Then the model of the evaluated object is established to get the health status of quantitative level. Finally, the method is used for health assessment of aircraft hydraulic pump .The results of simulation show the practicability of this method.

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