scholarly journals Equipment Operational Reliability Evaluation Method Based on RVM and PCA-Fused Features

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Linbo Zhu ◽  
Dong Chen ◽  
Pengfei Feng
Keyword(s):  
Experimental Test ◽  
Evaluation Method ◽  
Failure Time ◽  
Principal Component ◽  
Operational Reliability ◽  
Maintenance Cost ◽  
Hybrid Index ◽  
Failure Data ◽  
Vector Machines ◽  
Rotor Bearings

Reliability assessment is of great significance in ensuring the safety and reducing maintenance cost of equipment. The traditional statistical method is widely used to estimate the reliability of mass equipment; however, it cannot efficiently predict the overall reliability of single or small batch equipment due to lack of failure data. This paper introduced the operational reliability concept to describe the running condition of single or small batch equipment and proposed a method based on the combination of Relevance Vector Machines (RVMs) and Principal Component Analysis (PCA) to evaluate the operational reliability. Some representative characteristic indexes of operating equipment were firstly selected, and PCA was applied to obtain a hybrid index of the equipment’s running condition. Then, a RVM prediction model was trained to predict the development of the hybrid index and corresponding probability density function (PDF). Based on this, the operational reliability of the equipment was calculated by the interval integral defined by the failure threshold and the predicted value of the hybrid index. The approach was validated using the experimental test conducted on the aero-engine rotor bearings. The results show a good agreement in the evaluations of the failure time between the proposed method and the experimental test.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

scholarly journals Efficient Geo-Textual Hybrid Indexing Techniques for Moving Objects and Queries

2020 ◽  
Vol 8 (6) ◽  
pp. 4419-4428
Keyword(s):  
Moving Objects ◽  
Traffic Monitoring ◽  
Spatial Index ◽  
Index Structure ◽  
Maintenance Cost ◽  
Hybrid Index ◽  
Indexing Techniques ◽  
Inverted List ◽  
Textual Data ◽  
Future Position

Advancements of various Geographic Information Technologies have resulted in huge growth in Geo-Textual data. Many Indexing and searching algorithms are developed to handle this Geo-Textual data which contains spatial, textual and temporal information. In past, Indexing and searching algorithms are developed for the applications in which the object trajectory or velocity vector is known in advance and hence we can predict the future position of the objects. There are real time applications like emergency management systems, traffic monitoring, where the objects movements are unpredictable and hence future position of the objects cannot be predicted. Techniques are required to answer the geo-textual kNN query where the velocity vectors or trajectories of moving and moving queries are not known. In case of moving objects, capturing current position of the object and maintaining spatial index optimally is very much essential. The hybrid indexing techniques used earlier are based on R-tree spatial index. The nodes of the R-tree index structure are split or merged to maintain the locations of continuously moving objects, increasing the maintenance cost as compared to the grid index. In this paper a solution is proposed for creating and maintaining hybrid index for moving objects and queries based on grid and inverted list hybrid indexing techniques. The method is also proposed for finding Geo-Textual nearest neighbours for static and moving queries using hybrid index and conceptual partitioning of the grid. The overall gain reported by the experimental work using hybrid index over the non- hybrid index is 30 to 40 percent depending on the grid size chosen for mapping the data space and on the parameters of queries.

scholarly journals Multiple Process Capability Improvement Method based on Principal Component Analysis

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Guangqi Ying ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Yuxin Liu ◽  
Shengyong Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Evaluation Method ◽  
Process Capability ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Process ◽  
Improvement Method ◽  
Capability Evaluation ◽  
Capability Improvement

For the traditional multi-process capability construction method based on principal component analysis, the process variables are mainly considered, but not the process capability, which leads to the deviation of the contribution rate of principal component. In response to the question, this paper first clarifies the problem from two aspects: theoretical analysis and example proof. Secondly, aiming at the rationality of principal components degree, an evaluation method for pre-processing data before constructing MPCI using PCA is proposed. The pre-processing of data is mainly to standardize the specification interval of quality characteristics making the principal components degree more reasonable and optimizes the process capability evaluation method. Finally, the effectiveness and feasibility of the method are proved by an application example.

scholarly journals Cascade Support Vector Machines with Dimensionality Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Oliver Kramer
Keyword(s):  
Support Vector Machines ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Large Data ◽  
Locally Linear Embedding ◽  
Benchmark Problems ◽  
Support Vector ◽  
Training Set ◽  
Support Vectors ◽  
Vector Machines

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

scholarly journals Corrosion Detection in PSC Bridge Tendons Using Kernel PCA Denoising of Measured MFL Signals

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5984
Author(s):  
Chang Kook Oh ◽  
Changbin Joh ◽  
Jung Woo Lee ◽  
Kwang-Yeun Park
Keyword(s):  
Prestressed Concrete ◽  
Evaluation Method ◽  
Principal Component ◽  
Concrete Bridges ◽  
Kernel Principal Component Analysis ◽  
Cross Sectional ◽  
Measuring Device ◽  
Prestressed Concrete Bridges ◽  
Significant Damage ◽  
Non Destructive

The construction of prestressed concrete bridges has witnessed a steep increase for the past 50 years worldwide. The constructed bridges exposed to various environmental conditions deteriorate all along their service life. One such degradation is corrosion, which can cause significant damage if it occurs on the main structural components, such as prestressing tendons. In this study, a novel non-destructive evaluation method to incorporate a movable yoke system with denoising algorithm based on kernel principal component analysis is developed and applied to identify the loss of cross-sectional area in corroded external prestressing tendons. The proposed method using denoised output voltage signals obtained from the measuring device appears to be a reliable and precise monitoring system to detect corrosion with less than 3% sectional loss.

scholarly journals Assessing the annual risk of vehicles being hit by a rainfall-induced landslide: a case study on Kennedy Road in Wan Chai, Hong Kong

2020 ◽  
Vol 20 (6) ◽  
pp. 1833-1846 ◽  
Author(s):  
Meng Lu ◽  
Jie Zhang ◽  
Lulu Zhang ◽  
Limin Zhang
Keyword(s):  
Hong Kong ◽  
Failure Probability ◽  
Slope Failure ◽  
Failure Time ◽  
Suggested Method ◽  
Risk Level ◽  
Annual Number ◽  
Failure Data ◽  
Moving Vehicles

Abstract. Landslides threaten the safety of vehicles on highways. When analyzing the risk of a landslide hitting moving vehicles, the spacing between vehicles and the types of vehicles on the highway can be highly uncertain and have often been omitted in previous studies. Using a highway slope in Hong Kong as a case study, this paper presents a method for assessing the risk of moving vehicles being hit by a rainfall-induced landslide; this method also allows for the possible number of different types of vehicles hit by the landslide to be investigated. In this case study, the annual failure probability of the slope is analyzed based on historical slope failure data from Hong Kong. The spatial impact of the landslide is evaluated based on an empirical run-out prediction model. The consequences of the landslide are assessed using probabilistic modeling of the traffic, which can consider uncertainties in the vehicle spacing, vehicle types and slope failure time. Using the suggested method, the expected annual number of vehicles and people hit by the landslide can be conveniently calculated. This method can also be used to derive the cumulative frequency–number of fatalities curve for societal risk assessment. Using the suggested method, the effect of factors like the annual failure probability of the slope and the density of vehicles on the risk level of the slope can be conveniently assessed. The method described in this paper can provide a new guideline for highway slope design in terms of managing the risk of landslides hitting moving vehicles.

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