scholarly journals Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaoxi Ding ◽  
Liming Wang ◽  
Wenbin Huang ◽  
Qingbo He ◽  
Yimin Shao
Keyword(s):  
Health Monitoring ◽  
Clustering Analysis ◽  
Reference Model ◽  
Gaussian Model ◽  
Health Indicators ◽  
Active Role ◽  
Degradation Process ◽  
Performance Degradation ◽  
Monitoring Method ◽  
Feature Clustering

The health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. The corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. The experimental results show the potential in machine health performance degradation assessment.

Identification of Asynchronous Blade Vibration Parameters by Linear Regression of Blade Tip Timing Data

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Abbas Rohani Bastami ◽  
Pedram Safarpour ◽  
Arash Mikaeily ◽  
Mohammad Mohammadi
Keyword(s):  
Health Monitoring ◽  
Crack Detection ◽  
Turbine Blades ◽  
Sampling Rate ◽  
Health Indicators ◽  
Least Mean Square ◽  
Blade Vibration ◽  
Monitoring Method ◽  
Blade Tip ◽  
Vibration Parameters

Fracture of blades is usually catastrophic and creates serious damages in the turbomachines. Blades are subjected to high centrifugal force, oscillating stresses, and high temperature which makes their life limited. Therefore, blades should be checked and replaced at specified intervals or utilize a health monitoring method for them. Crack detection by nondestructive tests can only be performed during machine overhaul which is not suitable for monitoring purposes. Blade tip timing (BTT) method as a noncontact monitoring technique is spreading for health monitoring of the turbine blades. One of the main challenges of BTT method is identification of vibration parameters from one per revolution samples which is quite below Nyquist sampling rate. In this study, a new method for derivation of blade asynchronous vibration parameters from BTT data is proposed. The proposed method requires only two BTT sensors and applies least mean square algorithm to identify frequency and amplitude of blade vibration. These parameters can be further used as blade health indicators to predict defect growth in the blades. Robustness of the proposed method against measurement noise which is an important factor has been examined by numerical simulation. An experimental test was conducted on a bladed disk to show efficiency of the proposed method.

scholarly journals Fauna biodiversity as one of Repong Damar forest health indicators

2021 ◽  
Vol 886 (1) ◽  
pp. 012036
Author(s):  
Cici Doria ◽  
Rahmat Safe’i ◽  
Dian Iswandaru ◽  
Hari Kaskoyo
Keyword(s):  
Health Status ◽  
Health Monitoring ◽  
Forest Regeneration ◽  
Forest Health ◽  
Health Indicators ◽  
Health Condition ◽  
Monitoring Method ◽  
Forest Health Monitoring ◽  
Forest Sustainability

Abstract Repong Damar Pekon Pahmungan has a diverse fauna, especially primates. Primates have great benefits for forest sustainability, because the fruit seeds ingested by primates will help spread biodiversity and forest regeneration. The presence of primates can also be an indicator of forest health. The health condition of the repong damar forest is very influential on its sustainability so that one of the health indicators that can be used is biodiversity. Biodiversity of fauna can be identified by using the FHM (Forest Health Monitoring) method to determine the diversity and condition of its health status. Repong Damar has a diversity of primate fauna, namely long-tailed monkeys and gibbons found in cluster plots 3 and 5. Based on this, Repong Damar Pekon Pahmungan has poor forest health status.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

2013 ◽  
Vol 228 (10) ◽  
pp. 1743-1753 ◽  
Author(s):  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Friction Torque ◽  
Self Organizing Map ◽  
Wavelet Filter ◽  
Degradation Analysis ◽  
Space Mechanisms ◽  
Self Organizing

Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

scholarly journals Structural Health Monitoring Method of Pantograph–Catenary System Based on Strain Response Inversion

2021 ◽  
Vol 9 ◽  
Author(s):  
Sheng Liu ◽  
Yibo Wei ◽  
Yongxin Yin ◽  
Tangzheng Feng ◽  
Jinbao Lin
Keyword(s):  
Structural Health Monitoring ◽  
Contact Force ◽  
Health Monitoring ◽  
Data Consistency ◽  
Inversion Method ◽  
Maximum Speed ◽  
Strain Response ◽  
Monitoring Method ◽  
Structural Health ◽  
Catenary System

Pantograph-catenary system provides electric energy for the subway lines; its health status is essential to the serviceability of the vehicle. In this study, a real-time structural health monitoring method based on strain response inversion is proposed to calculate the magnitude and position of the dynamic contact force between the catenary and pantograph. The measurement principle, calibration, and installation detail of the fiber Bragg grating (FBG) sensors are also presented in this article. Putting this monitoring system in use, an application example of a subway with a rigid overhead catenary is given to demonstrate its performance. The pantograph was monitored and analyzed, running underground at a maximum speed of 80 km/h. The results show that the strain response inversion method has high measurement accuracy, good data consistency, and flexibility on sensor installation. It can accurately calculate the magnitude and location of the contact force exerted on the pantograph.

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