A Data Mining Strategy For Predictive Maintenance In The Operations Industry/ (Record no. 9785)
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| 000 -LEADER | |
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| fixed length control field | 10552nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 201210b2022 a|||f bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mina Magdy Mamdouh Kamel |
| 245 1# - TITLE STATEMENT | |
| Title | A Data Mining Strategy For Predictive Maintenance In The Operations Industry/ |
| Statement of responsibility, etc. | Mina Magdy Mamdouh Kamel |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 100 p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| Materials specified | Supervisor: <br/>Nashwa Abdelbaki |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2022 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>CERTIFICATION OF APPROVAL ...................................................................... iii<br/>DEDICATION ....................................................................................................... vii<br/>ACKNOWLEDGEMENTS .................................................................................... ix<br/>TABLE OF CONTENTS ........................................................................................ xi<br/>LIST OF FIGURES ............................................................................................... xv<br/>LIST OF TABLES ............................................................................................... xvii<br/>ACRONYMS ........................................................................................................ xix<br/>PUBLICATIONS .................................................................................................. xxi<br/>ABSTRACT ........................................................................................................ xxiii<br/>1. INTRODUCTION ............................................................................................ 1<br/>1.1. Motivation ................................................................................................. 1<br/>1.2. Thesis Outline ........................................................................................... 4<br/>2. Background Overview and Related Work ........................................................ 5<br/>2.1. Background ............................................................................................... 5<br/>2.1.1. Mobile Network Definitions .................................................................. 6<br/>2.1.2. Access Network Operation System ....................................................... 8<br/>2.1.3. Mobile Network Alarm Types and Main Definitions ........................... 8<br/>xii<br/>2.1.4. Maintenance in Mobile Networks ......................................................... 9<br/>2.1.5. Preventive Maintenance ...................................................................... 11<br/>2.1.6. Predictive Maintenance ....................................................................... 12<br/>2.2. Historical work ........................................................................................ 15<br/>2.2.1. Classification State of Art.................................................................... 15<br/>2.2.2. Long Short-Term Memory State of Art ............................................... 17<br/>2.2.3. Association Rules State of Art............................................................. 18<br/>2.2.4. Our Observations ................................................................................. 20<br/>3. ASSOCIATION RULE MINING ALGORITHMS ....................................... 21<br/>3.1. Principle of Association Rule Mining ..................................................... 22<br/>3.2. Association Rules Efficiency Measurements .......................................... 22<br/>3.3. Association Rule Models ........................................................................ 24<br/>3.3.1. Apriori Algorithm Illustration ............................................................. 25<br/>3.3.2. Frequent Pattern (FP) Growth Algorithm Illustration ......................... 25<br/>3.4. Apriori vs FP Growth .............................................................................. 26<br/>4. SYSTEM MODEL ......................................................................................... 27<br/>4.1. Data Challenges ......................................................................................... 27<br/>4.1.1. Dataset Size Challenge .......................................................................... 27<br/>4.1.2. Flapping Challenge ................................................................................. 29<br/>4.2. Model Structure Challenge...................................................................... 30<br/>4.3. Model Requirements ............................................................................... 32<br/>4.3.1 Time Challenge of The Event to Predict ............................................. 33<br/>4.3.2. Problem Prediction with Recommendation Action ............................. 34<br/>4.3.3. Computational Power Issue ................................................................. 35<br/>4.4. Approach Structure ................................................................................. 35<br/>4.5. Operation Flow ........................................................................................ 37<br/>4.5.1 Data Collection .................................................................................... 37<br/>4.5.2 Handling Outliers ...................................................................................... 37<br/>4.5.3. Target Event and Hidden Interval ....................................................... 40<br/>4.5.4. Select Historical Window .................................................................... 41<br/>4.6. Feature Engineering ................................................................................ 42<br/>4.6.1. Feature Engineering for Historical Alarms Behavior .......................... 43<br/>4.6.2. Feature Engineering Over Historical and Target alarm ....................... 46<br/>4.7. Running Over Spark FP-Growth ............................................................. 48<br/>4.8. Iteration ................................................................................................... 48<br/>4.9. Testing and Training Data Flow.............................................................. 48<br/>5. EXPERIMENTS AND RESULT ................................................................... 51<br/>5.1. Experiments related to previous work ..................................................... 51<br/>5.1.1. Classification Experiments .................................................................. 51<br/>5.1.2. Long Short-Term Memory Experiments ............................................. 53<br/>5.1.3. Association Rules Experiments ........................................................... 55<br/>5.2. New Model Experiments ......................................................................... 57<br/>5.2.1. Phase 1 Build a Proof of Concept (POC) ............................................ 57<br/>5.2.2. Phase 2 Building The logic over Network Scale ................................. 60<br/>5.3. RESULTS................................................................................................ 62<br/>6. CONCLUSION AND FUTURE WORK ....................................................... 67<br/>References .............................................................................................................. 69 |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>Service-providing companies such as mobile networks use a massive amount of different hardware devices to support their customers. They build a maintenance process to have better quality management and technical advancements. Risks from hardware failure, security, and environmental issues must be controlled. The industry focuses on this process since it is directly responsible for the effectiveness and sustainability of the industrial operation. Maintenance plays a main role in organizational structures, operation cost, and customer experience.<br/>Nowadays, the industry directly focuses on data and knowledge discovery processes. Sources like the Internet of Things contain a massive number of sensors. Each of these devices generates a large amount of data in real-time. Big data clusters provided a platform to accommodate this large amount of data. Data mining in many types of research has proven to be a powerful tool that can analyze large data from industrial sources. This has an advantage over manual analytics since it easily merges data from different sources to clarify hidden relations for better data understanding.<br/>The operational maintenance process collects data from different hardware components in the industry. This large number of components generates data in real-time. This leads to a massive amount of data. The modern operation process applies the knowledge discovery methods over this massive amount of data. This generates relations between these data sources and provides a solution to the existing problems. Big data systems used a complicated parsing process to transform the formats into standard ones.<br/>Predictive maintenance is one of the top rising trends, especially after the recent vibe of Machine Learning (ML), which introduced many powerful tools that can be used in event prediction. Predictive maintenance increases the efficiency of<br/>xxiv<br/>involved machines in the system by predicting future faults. This is different from reactive maintenance which allows assets to run to failure.<br/>Different techniques of Machine Learning like classification or data mining are used to predict future alarms. However, one of the missing aspects is to predict the future alarm and provide a recommendation for the required action.<br/>In our thesis, we propose a methodology to apply data mining over the historical trend of generated events from the telecom system. This data mining process generates rules to predict future failures. Our methodology recommends the required actions to avoid future failure. It predicts alarms that will happen after a time period from 12 to 24 hours. This means our system guarantees to predict future failure with a sufficient time window to apply the recommended action. Our approach can deal with any sort of available logging system. We did a fine tuning to adjust the data formats and feature engineering steps to show hidden features required for a better model. We customized new parameters to explain the model gain. |
| 546 ## - Language Note | |
| Language Note | Text in English, abstracts in English. |
| 650 #4 - Subject | |
| Subject | Informatics-IFM |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 650 #4 - Subject | |
| -- | 266 |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| 690 ## - Subject | |
| -- | 266 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Main library | Main library | 09/14/2022 | 610/ M.K.D / 2022 | 09/14/2022 | 09/14/2022 | Thesis |