Automobile Predictive Maintenance Using Deep Learning

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

Download Full-text

Diagnosis and Predictive Maintenance of Machinery and Equipment, by Measuring Vibration

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.325-326.186 ◽

2013 ◽

Vol 325-326 ◽

pp. 186-191

Author(s):

Dan Florin Niculescu ◽

Adrian Ghionea ◽

Adrian Olaru

Keyword(s):

Preventive Maintenance ◽

Kinematic Chain ◽

Predictive Maintenance ◽

Maintenance Management ◽

Machine Precision ◽

Predictive Diagnosis ◽

Maintenance Program ◽

Automatic Machinery ◽

A Company ◽

Made In

The paper presents results of analysis of the dynamic behavior of rotating mechanisms and couplings of the main structure of a kinematic chain sharpening machine precision Cugir normal UAS 200. The ultimate goal is to establish diagnostic and maintenance program the car UAS 200. Diagnosis by measurement of vibration and noise, allow a company to monitor faults and machines and equipment, through a system of preventive maintenance, predictive. Diagnosis automatic machinery and equipment is made in order to ensure a higher reliability of these and how to obtain a more extended life cycle without the occurrence of defects. The application of preventive and predictive maintenance management supports enterprise, because it proves effective, the information you provide in making decisions.

Download Full-text

SIGNIFICANCE OF BUILDING MAINTENANCE MANAGEMENT SYSTEM TOWARDS SUSTAINABLE DEVELOPMENT: A REVIEW

Journal of Engineering Studies and Research ◽

10.29081/jesr.v21i1.41 ◽

2016 ◽

Vol 21 (1) ◽

Cited By ~ 3

Author(s):

MD AZREE OTHUMAN MYDIN

Keyword(s):

Sustainable Development ◽

Preventive Maintenance ◽

Management System ◽

Early Stage ◽

Major Elements ◽

Predictive Maintenance ◽

Maintenance Management ◽

Sustainable Building ◽

Building Maintenance ◽

Maintenance Work

<p>The notion of building maintenance is separated into two elements specifically building maintenance management and building maintenance technology. Building maintenance management should accurately be considered as unfolding how a system of maintenance endeavor might be prearranged to deal with a problem of building maintenance. It distinguishes that, aside from locating and remedying the building deficiency an effective programme to restrain overall cost of maintenance but also in an effort to maximize the gain and benefits of the savings. There are a few factors that influence the decision to accomplish the maintenance work. There are the maintenance needs that is the principal aspire of maintenance is to protect a building in its early stage and some major rationale for maintaining building are retaining its significance and value of investments, maintaining the building in a condition that it persists to accomplish its purpose and presenting a good outer shell. Besides that, the efficient maintenance management system embraces many skills and efforts that include identifying maintenance needs and the accurate and spot on remedies. In general, there have four types of maintenance which are breakdown or corrective maintenance, scheduled maintenance, preventive maintenance and also predictive maintenance. This paper will review and discuss some of the major elements of building maintenance towards achieving sustainable building.</p>

Download Full-text

A modularized framework for predictive maintenance scheduling

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x11431209 ◽

2011 ◽

Vol 226 (4) ◽

pp. 380-391 ◽

Cited By ~ 1

Author(s):

Ming-Yi You ◽

Guang Meng

Keyword(s):

Preventive Maintenance ◽

Industrial Applications ◽

Maintenance Scheduling ◽

Predictive Maintenance ◽

Scheduling Model ◽

Component Systems ◽

Maintenance Policies ◽

The Individual ◽

Maintenance Schedules ◽

Dynamic Updating

This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.

Download Full-text

High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model

Neural Computing and Applications ◽

10.1007/s00521-020-05561-8 ◽

2021 ◽

Author(s):

Xiaoxiong Zheng ◽

Tao Chen

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Binary Classification ◽

Remote Sensing Image ◽

Classification Model

Download Full-text

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽

10.3390/molecules26010020 ◽

2020 ◽

Vol 26 (1) ◽

pp. 20

Author(s):

Reynaldo Villarreal-González ◽

Antonio J. Acosta-Hoyos ◽

Jaime A. Garzon-Ochoa ◽

Nataly J. Galán-Freyle ◽

Paola Amar-Sepúlveda ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Real Time ◽

Binary Classification ◽

Simulated Data ◽

Classification Model ◽

Rt Pcr ◽

Simon Bolivar ◽

Polymerase Chain ◽

Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Download Full-text