scholarly journals Study on Health Assessment Method of a Braking System of a Mine Hoist

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 769 ◽  
Author(s):  
Juanjuan Li ◽  
Guoying Meng ◽  
Guangming Xie ◽  
Aiming Wang ◽  
Jun Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Health Management ◽  
Back Propagation ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
Comprehensive Assessment ◽  
Fuzzy Comprehensive Assessment ◽  
Braking System ◽  
Mine Hoist

This paper presents a method for calculating the health degree (HD) of a braking system of a mine hoist combined with three-level fuzzy comprehensive assessment (TLFCA) and a back-propagation neural network (BPNN). Firstly, the monitored values of a sensor are fused by multi-time fusion and the fuzzy comprehensive assessment values (FCAVs) of the health condition (HC) of the sensor are obtained. Secondly, the FCAVs of all sensors in a subsystem are fused by multi-sensor fusion, and FCAVs of the subsystem are obtained. Then the FCAVs of all subsystems are fused by multi-subsystem fusion and FCAVs of the system are obtained. All the FCAVs are fed into a pre-trained neural network, and the corresponding HD of the sensor, subsystem and system is obtained. Finally, the practicability, reliability and sensitivity of the proposed method are verified by the monitored values of the test rig. This paper presents a method to provide technical support for intelligent maintenance, and also provides necessary data for further prognostics health management (PHM) of the braking system. The method presented in this paper can also be used as a reference for the HD calculation of the whole hoist and other complicated equipment.

Fault diagnosis method of mine hoist braking system based on deep convolution neural network

2021 ◽  
Author(s):  
Fangyuan Yan ◽  
Juanli Li ◽  
Dong Miao ◽  
Qi Cao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Descent Method ◽  
Convolution Neural Network ◽  
Monitoring Data ◽  
Gradient Descent Method ◽  
Braking System ◽  
Diagnosis Method ◽  
Mine Hoist

Abstract A reliable braking system is an important guarantee for safe operation of mine hoist. In order to make full use of the monitoring data in the operation process of mine hoist, identify the operation status of the hoist, and further carry out fault diagnosis on it, the deep learning method was introduced into the fault diagnosis of the hoist, and a fault diagnosis method of hoist braking system based on convolution neural network has been proposed. Firstly, the working principle and fault mechanism of disc brake and its hydraulic station in hoist braking system are analyzed, and the monitoring parameters of this study are determined; then, based on massive monitoring data, the convolutional neural networks (CNN) is established, the one-dimensional signal collected by the sensor is transformed into two-dimensional image for coding, the neural network is trained by gradient descent method, and the network structure parameters are modified according to the training results. Finally, the fault diagnosis model is compared and verified by using the sample set based on the traditional back propagation neural network (BP) and CNN. The results show that the accuracy of CNN is higher than that of BP, and the accuracy rate can reach 99.375% after reducing the involvement between samples. This method can make full use of the monitoring data for diagnosis, without subjective intervention of experts, and improve the accuracy of diagnosis.

Health Condition Assessment of Gas Turbine Generator on Offshore Platform

2017 ◽  
Author(s):  
Yunpeng Cao ◽  
Minghao Wu ◽  
Qingcai Yang ◽  
Shuying Li ◽  
Dongyang Yan ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Quantitative Assessment ◽  
Health Assessment ◽  
Health Condition ◽  
Comprehensive Assessment ◽  
Assessment Model ◽  
Turbine Generator ◽  
Fuzzy Comprehensive Assessment ◽  
Criticality Analysis ◽  
Hierarchy Process

Based on the function level of a gas turbine generator, this paper presents a method for the quantitative assessment of the health condition of a gas turbine generator by using fuzzy comprehensive assessment (Fuzzy) and an analysis hierarchy process (AHP). First, the failure mode, effects and criticality analysis method (FMECA) is used to construct the assessment framework and obtain the indicator set for the health assessment. The membership degree of each indicator is realized by using triangular and trapezoidal membership functions. Second, the fuzzy comprehensive assessment model with analysis hierarchy process (Fuzzy-AHP) is established to implement the quantitative assessment of the health condition of the gas turbine generator, and the man-made influence on the weight assignment is decreased. Finally, an example of a three-shaft gas turbine generator health assessment is provided to validate and prove that the method is effective and feasible.

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.

scholarly journals Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 803 ◽  
Author(s):  
Yung-Hui Li ◽  
Muhammad Saqlain Aslam ◽  
Kai-Lin Yang ◽  
Chung-An Kao ◽  
Shin-You Teng
Keyword(s):  
Deep Learning ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
The Body ◽  
Convolutional Network ◽  
Non Invasive ◽  
Body Constitution ◽  
Perfect Symmetry ◽  
Psychological Strength

There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy.

Research on a Nonlinear Fuzzy Comprehensive Assessment Method for Oil & Gas Pipeline Failure Based on Fault Tree Analysis

2012 ◽  
Vol 187 ◽  
pp. 304-310
Author(s):  
Xue Wu ◽  
Ci Yuan Xiao ◽  
Xue Yan Xu
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Engineering Application ◽  
Assessment Method ◽  
Comprehensive Assessment ◽  
Gas Pipeline ◽  
Combination Method ◽  
Fuzzy Comprehensive Assessment ◽  
Tree Analysis ◽  
Pipeline Failure

The combination method of fault tree analysis and nonlinear fuzzy comprehensive assessment method was proposed to make research of oil & gas pipeline failure .The common factors influencing oil & gas pipeline failure could be determined with fault tree analysis .However , the practical operated oil & gas pipeline often have some individual factors and fuzzy ones .Nonlinear fuzzy comprehensive assessment method could evaluate objectively based on evaluated factors sets and weight sets provided by fault tree analysis .The new model steps were listed by taking the example of oil & gas pipeline failure .The result indicates that the method is more reasonable and easier for engineering application , and the evaluation result is more with the objective reality.

Determination of Occurrence Grades for Factors Affecting Congelation Failure on Basis of Fuzzy Comprehensive Assessment Method

2013 ◽  
Vol 753-755 ◽  
pp. 2916-2919
Author(s):  
Yang Wang ◽  
Qing Lin Cheng ◽  
Xian Li Li ◽  
Xi Yi
Keyword(s):  
Assessment Method ◽  
Comprehensive Assessment ◽  
Scientific Management ◽  
Pipeline Transportation ◽  
Fuzzy Comprehensive Assessment ◽  
Factors Affecting ◽  
Oil Pipeline ◽  
Improvement Measures ◽  
Financial Losses

Pipeline transportation is the most important transport mode for crude oil. When oil pipeline operates at low throughput or shutdown, congelation accident always happen as the temperature of oil in pipeline drops, which causes malignant accidents and significant financial losses. Study of the various factors affecting congelation failure is necessary. According to the statistics and analysis of congelation accident, direct and indirect reasons of affecting pipeline congelation failure were determined. Combining with fuzzy comprehensive assessment method, the related importance of factors on congelation accident was studied, and the grade of factors affecting failure congelation of pipeline was also identified. Aiming at the factors, some improvement measures were put forward. These can provide evidences for accident treatment, planned maintenance, security operation and scientific management.

scholarly journals Comparing methods for assessing the health of regeneration in Scots pine culture

2020 ◽  
Vol 81 (2) ◽  
pp. 51-64
Author(s):  
Zbigniew Sierota ◽  
Monika Małecka ◽  
Marta Damszel
Keyword(s):  
Scots Pine ◽  
Fungal Pathogen ◽  
Abiotic Factors ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
Monitoring Method ◽  
Surface Method ◽  
Reliable Assessment ◽  
Field Assessment

Abstract This study’s aim was to describe the health condition of Scots pine cultures of up to 10 years old using and comparing various field assessment methods. Since forest districts report on the health of stands annually, we assumed that for a proper health analysis it is necessary to develop a simple and yet reliable assessment method that allows for determining the share of fungal pathogen infection in the stand (both foliar and root pathogens) and their differentiation from symptoms of abiotic factors such as drought. Six different methods of health assessment were tested in selected Forest Districts across Poland. We found that the most reliable assessment of the health condition of young stands is obtained with the surface method ‘MF’ (phytopathological monitoring method) and the linear ‘Z’ method, which uses transects of 30 meters in three rows in the shape of the letter Z.

scholarly journals Three-Stage Wind Turbine Assessment Method: Condition Monitoring, Failure Prediction, And Health Assessment

2021 ◽  
Author(s):  
Yongsheng Qi ◽  
Tongmei Jing ◽  
Chao Ren ◽  
Xuejin Gao
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Health Management ◽  
Health Assessment ◽  
Failure Prediction ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Generalized Regression Neural Network ◽  
Monitoring Method

Abstract To improve the wind turbine shutdown early warning ability, we present a generalized model for wind turbine (WT) prognosis and health management (PHM) based on the data collected from the SCADA system. First, a new condition monitoring method based on kernel entropy component analysis (KECA) was developed for nonlinear data. Then, an aggregate statistic T was designed to express the state change of the monitoring parameters. As the features were submerged because of the diversity and nonlinearity of SCADA data, an enhanced generalized regression neural network (GRNN) method—KECA-GRNN—for failure prediction was developed by adding KECA for feature extraction to improve the predictive performance. Finally, the results of the KECA-GRNN model were visualized by a bubble chart, which made the health assessment results of the WT more intuitive. Similarly, the fusion residual was defined to analyze the health trend of the WT, and the health status of the WT was represented by two visualization methods—bubble chart and fuzzy comprehensive evaluation. Furthermore, they were evaluated using SCADA data that were collected from a wind farm. Observations from the results of the model indicated the ability of the approach to trend and assess turbine degradation before known downtime occurrences.

scholarly journals PSIII-6 Analysis of vocal sounds for detecting abnormal symptoms in sows

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 189-190
Author(s):  
Donghoon Lee ◽  
Kwongoo Bum ◽  
Sang-Hyon Oh
Keyword(s):  
Fixed Effects ◽  
Animal Health ◽  
Health Assessment ◽  
Strong Relationship ◽  
Acoustic Vibration ◽  
Assessment Method ◽  
Health Condition ◽  
Fast Transform ◽  
Health Assessments ◽  
Integrated Processing

Abstract Various technologies for animal health have been introduced and used in the livestock field as a part of an integrated processing methodology to construct a successful smart farm. This study aims to present a health assessment method applied to an individual pig using acoustic vibration. The experiment was based on the hypothesis that there is a strong relationship between acoustic phenotype and health condition. The information from a normal and abnormal sow was simultaneously and continuously recorded using a sound recorder for 24 hours. The abnormal sow was given an injection of 70% dextrose to the knee, which experienced necrosis due to a subsequent osmotic phenomenon. The experiment began at 9 am and continued until 8 am the next day and was repeated twice. During the experiment, the high-resolution recorder was located 50 cm from the top of the farrowing crate and directed at the sow’s head to reduce interferences from other sources of sound and noise. The first step of analysis was denoising the recorded acoustic information. Then, the Fourier fast transform was applied to the preprocessed data. Data were analyzed with PROC GLM (SAS 9.3), where a trial and treatment were included as fixed effects. The magnitude of frequency between normal and abnormal sows was significantly different (P < 0.05), in which the range of magnitude value was higher and lower than 0.015 for the normal and abnormal sow, respectively. The range 0.015 to 0.020 for the normal sow was clearly discriminated from the range 0.010 to 0.015 for the abnormal one. A more accurate interpretation of sows’ vocal data depends on the quality and quantity of data regarding their health condition. A promising algorithm of processing acoustic phenotype related to bio information could be useful in numerous complex health assessments.

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