scholarly journals Bearing Health Assessment Based on Chaotic Characteristics

2013 ◽  
Vol 20 (3) ◽  
pp. 519-530 ◽  
Author(s):  
Chen Lu ◽  
Qian Sun ◽  
Laifa Tao ◽  
Hongmei Liu ◽  
Chuan Lu
Keyword(s):  
Health Assessment ◽  
Vibration Signal ◽  
Assessment Method ◽  
Largest Lyapunov Exponent ◽  
Health State ◽  
Self Organizing Map ◽  
Linear Complex ◽  
Vibration Signals ◽  
Confidence Value

Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the selected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM). Both Grassberger-Procaccia algorithm and Takens' theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value (CV) which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing.

scholarly journals Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

scholarly journals Research into Power Transformer Health Assessment Technology Based on Uncertainty of Information and Deep Architecture Design

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuguo Gao ◽  
Jun Zhao ◽  
Yunpeng Liu ◽  
Ziqiang Xu ◽  
Zhe Li ◽  
...  
Keyword(s):  
Power Transformer ◽  
Cloud Model ◽  
Health Assessment ◽  
Assessment Method ◽  
Actual Case ◽  
Defect Type ◽  
Defect Location ◽  
Deep Architecture ◽  
High Conflict

The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21%, which verifies the practicability and efficiency of the method.

scholarly journals Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2086 ◽  
Author(s):  
Fengdi Liu ◽  
Yihai He ◽  
Yixiao Zhao ◽  
Anqi Zhang ◽  
Di Zhou
Keyword(s):  
Health Risk ◽  
High Reliability ◽  
Health Assessment ◽  
Assessment Method ◽  
Predictive Maintenance ◽  
Assembly System ◽  
Health State ◽  
Maintenance Strategy ◽  
Health States ◽  
Operational Data

Assembly quality is the barometer of assembly system health, and a healthy assembly system is an important physical guarantee for producing reliable products. Therefore, for ensuring the high reliability of products, the operational data of the assembly system should be analyzed to manage health states. Therefore, based on the operational data of the assembly system collected by intelligent sensors, from the perspective of quality control based on risk thinking, a risk-oriented health assessment method and predictive maintenance strategy for managing assembly system health are proposed. First, considering the loss of product reliability, the concept of assembly system health risk is proposed, and the risk formation mechanism is expounded. Second, the process variation data of key reliability characteristics (KRCs) collected by different sensors are used to measure and assess the health risk of the running assembly system to evaluate the health state. Third, the assembly system health risk is used as the maintenance threshold, the predictive maintenance decision model is established, and the optimal maintenance strategy is determined through stepwise optimization. Finally, the case study verifies the effectiveness and superiority of the proposed method. Results show that the proposed method saves 37.40% in costs compared with the traditional method.

WTG’s Gearbox Condition Assessment by Matching the Hilbert-Huang Spectrum of Vibration Signal

2013 ◽  
Vol 291-294 ◽  
pp. 522-526
Author(s):  
Hai Ning Pan ◽  
Ming Qin ◽  
Lei Pan
Keyword(s):  
Vibration Signal ◽  
Condition Assessment ◽  
Assessment Method ◽  
Iterative Closest Point ◽  
Intrinsic Mode Functions ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Icp Algorithm ◽  
Wind Turbine Generator ◽  
Mode Decomposition

A gearbox condition assessment method for the Wind Turbine Generator (WTG) is proposed. Vibration signal’s Intrinsic Mode Functions (IMF) are decomposed by Empirical Mode Decomposition (EMD). Normalized Hilbert-Huang and Direct Quadrature (DQ) method are used to determine the instantaneous frequency. The HHS of vibration signals is plotted and then is shifted to match the pre-defined faulty gear condition by the Iterative Closest Point (ICP) algorithm to diagnose their similarities. The principle and effectiveness of the proposed method are illustrated by simulation, the fault types of gearbox can be identified by ICP algorithm effectively.

scholarly journals Indoor Environmental Health Assessment in Eco-Building and Its Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 794
Author(s):  
Jing Yuan ◽  
Xiaohui Zhao ◽  
Giwa Abdulmoseen Segun ◽  
Mohammadtaghi Vakili ◽  
Lexuan Zhong
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Indoor Environment ◽  
Health Assessment ◽  
Health Impact ◽  
Assessment Method ◽  
Strategic Decision ◽  
Pollution Level ◽  
Indoor Environmental Quality

It is necessary to consider all aspects of environmental factors when assessing the health impact of an eco-building environment on its occupants. However, the multi-criteria and imprecise nature of the indoor-environment in the eco-buildings has caused difficulties in quantifying the indoor environmental pollution level. This paper describes the optimal classification and priority weight methods, which are particularly useful for assessing the indoor environmental quality (IEQ) of an eco-building to demonstrate its innovative applications. The analytic hierarchy process (AHP) was used to set up the strategic decision-making evaluation system for computing the indoor environment index (IEI) risk ranking of eco-buildings. Combined with this, a Microsoft Delphi-based IEQ intelligent forecasting software simulations package was developed, and the innovative application of indoor environmental comprehensive assessment was verified by a case study in Shanghai. The evaluation result was analyzed by the priority weight methods and the AHP decision-making system noted above. This health assessment method and system provides an innovative way for the indoor environment risk evaluation of eco-buildings and is helpful to standardize the local building market.

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 A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 953 ◽  
Author(s):  
Paolo Casoli ◽  
Mirko Pastori ◽  
Fabio Scolari ◽  
Massimo Rundo
Keyword(s):  
Vibration Signal ◽  
Computational Effort ◽  
Classification Algorithm ◽  
Condition Based Maintenance ◽  
Fault Identification ◽  
Health State ◽  
Vibration Signals ◽  
Axial Piston Pumps ◽  
Variable Displacement ◽  
Axial Piston

In recent years, the interest of industry towards condition-based maintenance, substituting traditional time-based maintenance, is growing. Indeed, condition-based maintenance can increase the system uptime with a consequent economic advantage. In this paper, a solution to detect the health state of a variable displacement axial-piston pump based on vibration signals is proposed. The pump was tested on the test bench in different operating points, both in healthy and faulty conditions, the latter obtained by assembling damaged components in the pump. The vibration signals were acquired and exploited to extract features for fault identification. After the extraction, the obtained features were reduced to decrease the computational effort and used to train different types of classifiers. The classification algorithm that presents the greater accuracy with reduced features was identified. The analysis has also showed that using the time sampling raw signal, a satisfying accuracy could be obtained, which will permit onboard implementation. Results have shown the capability of the algorithm to identify which fault occurred in the system (fault identification) for each working condition. In future works, the classification algorithm will be implemented onboard to validate its effectiveness for the online identification of the typical incipient faults in axial-piston pumps.

Storm surge risk assessment method for a coastal county in China: case study of Jinshan District, Shanghai

2020 ◽  
Vol 34 (5) ◽  
pp. 627-640 ◽  
Author(s):  
Shi Xianwu ◽  
Qiu Jufei ◽  
Chen Bingrui ◽  
Zhang Xiaojie ◽  
Guo Haoshuang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Storm Surge ◽  
Assessment Method ◽  
Risk Assessment Method ◽  
Coastal County

scholarly journals A Capabilities-Led Approach to Assessing Technological Solutions for a Rural Community

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1398
Author(s):  
Xinfang Wang ◽  
Rosie Day ◽  
Dan Murrant ◽  
Antonio Diego Marín ◽  
David Castrejón Botello ◽  
...  
Keyword(s):  
Rural Community ◽  
Rural Areas ◽  
Assessment Method ◽  
Unintended Consequences ◽  
Energy Services ◽  
Grid Systems ◽  
Technological Interventions ◽  
Solution Design ◽  
Improve Access

To improve access to affordable, reliable and sustainable energy in rural areas of the global south, off-grid systems using renewable generation and energy storage are often proposed. However, solution design is often technology-driven, with insufficient consideration of social and cultural contexts. This leads to a risk of unintended consequences and inappropriate systems that do not meet local needs. To address this problem, this paper describes the application of a capabilities-led approach to understanding a community’s multi-dimensional energy poverty and assessing their needs as they see them, in order to better design suitable technological interventions. Data were collected in Tlamacazapa, Mexico, through site visits and focus groups with men and women. These revealed the ways in which constrained energy services undermined essential capabilities, including relating to health, safety, relationships and earning a living, and highlighted the specific ways in which improved energy services, such as lighting, cooking and mechanical power could improve capabilities in the specific context of Tlamacazapa. Based on these findings, we propose some potential technological interventions to address these needs. The case study offers an illustration of an assessment method that could be deployed in a variety of contexts to inform the design of appropriate technological interventions.

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