Planned Maintenance Schedule Update Method for Predictive Maintenance of Semiconductor Plasma Etcher

The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturing-related processes, e.g., predictive maintenance. Existing predictive maintenance studies generally focus on either a predictive model without considering the maintenance decisions or maintenance optimizations based on the degradation models of the known system. To address this, we propose PMMI 4.0, a Predictive Maintenance Model for Industry 4.0, which utilizes a newly proposed solution PMS4MMC for supporting an optimized maintenance schedule plan for multiple machine components driven by a data-driven LSTM model for RUL (remaining useful life) estimation. The effectiveness of the proposed solution is demonstrated using a real-world industrial case with related data. The results showed the validity and applicability of this work.

Download Full-text

Interval Waktu Pemeliharaan Berdasarkan Reliability Centered Maintanance (RCM) Pada Kinerja Saluran Udara Tegangan Menengah (SUTM) Perusahaan Listrik Negara

Jurnal Media Teknik dan Sistem Industri ◽

10.35194/jmtsi.v5i2.1459 ◽

2021 ◽

Vol 5 (2) ◽

pp. 110

Author(s):

Very Fernando ◽

Hernadewita Hernadewita ◽

Humiras Hardi Purba

Keyword(s):

Statistical Data ◽

Predictive Maintenance ◽

Time Intervals ◽

Overhead Lines ◽

Medium Voltage ◽

Maintenance Schedule ◽

Reliability Centered Maintenance ◽

Maintenance Time ◽

Data Statistics ◽

Cable Lines

The State Electricity Company (PLN) serves all Indonesian people from Sabang to Merauke with overhead lines and cable lines. The frequency of blackouts in an area due to disturbances in the channel. PLN seeks to suppress disturbances by carrying out maintenance, namely turning it off or without being extinguished. Medium Voltage Air Lines (SUTM) are more frequent disturbances. The worst damage happened to the conductors, Jumpers, and insulators. Damage will often occur if it is not predictable. In order to find out when damage occurs, a method based on Predictive Maintenance is used, namely maintenance based on historical disturbance data statistics. Predictive Maintenance can be realized by using Reliability Centered Maintenance (RCM). From the results of RCM calculations and statistical data on one PLN unit, there is a new value for predictive maintenance time intervals, for conductors maintenance schedule must be carried out every 2 days, Jumpers every 12 days, and isolators every 16 days before serious damage occurs and widespread blackout Perusahaan Listrik Negara (PLN) melayani seluruh masyarakat Indonesia dari Sabang sampai Merauke dengan saluran udara dan saluran kabel. Frekuensi terjadi pemadaman pada suatu wilayah karena adanya gangguan pada saluran tersebut. PLN berupaya untuk menekan gangguan dengan melakukan pemeliharaan, yaitu dipadamkan atau tanpa dipadamkan. Saluran Udara Tegangan Menengah (SUTM) lebih sering terjadi gangguan. Kerusakan terparah ternyata terjadi pada konduktor, Jumper dan isolator. Kerusakan akan sering terjadi apabila tidak mampu diprediksi. Untuk dapat mengetahui kapan akan terjadi kerusakan digunakan metode berbasis Predictive Maintanance, yaitu pemeliharaan berdasarkan statistik data historis gangguan. Predictive Maintenance dapat terwujud dengan menggunakan Relliability Centered Maintanance (RCM). Dari hasil perhitungan RCM dan data statsik pada salah satu unit PLN, maka terdapat nilai baru intuk interval waktu pemeliharaan secara predictive, untuk konduktor harus dilakukan penjadwalan pemeliharaan setiap 2 hari, Jumper setiap 12 hari, dan Isolator setiap 16 hari sebelum terjadi kerusakan yang parah dan pemadaman meluas.

Download Full-text

Principal component analysis technique for early fault detection

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189755 ◽

2021 ◽

pp. 1-12

Author(s):

Kumari Sarita ◽

Ramesh Devarapalli ◽

Sanjeev Kumar ◽

H. Malik ◽

Fausto Pedro García Márquez ◽

...

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Condition Monitoring ◽

Motor System ◽

Principal Component ◽

Component Analysis ◽

Predictive Maintenance ◽

Maintenance Schedule ◽

Complete Breakdown ◽

Early Fault Detection

Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis.

Download Full-text

The Convergence between Predictive Maintenance and Augmented Reality to Aid Renewable Energy Equipment Availability

International Journal on Engineering Applications (IREA) ◽

10.15866/irea.v2i5.1916 ◽

2014 ◽

Vol 2 (5) ◽

pp. 152

Author(s):

José Manuel Torres Farinha ◽

Inácio Adelino Fonseca ◽

Rúben Silva Oliveira ◽

Fernando Maciel Barbosa

Keyword(s):

Renewable Energy ◽

Augmented Reality ◽

Predictive Maintenance ◽

Equipment Availability ◽

Energy Equipment

Download Full-text

Experimental Investigation of Interaction between Electron Beams and Semiconductor Plasma

Telecommunications and Radio Engineering ◽

10.1615/telecomradeng.v64.i10.60 ◽

2005 ◽

Vol 64 (10) ◽

pp. 841-850 ◽

Cited By ~ 1

Author(s):

O. A. Zamuraev ◽

I. V. Lopatin ◽

A. F. Rusanov

Keyword(s):

Experimental Investigation ◽

Electron Beams ◽

Semiconductor Plasma

Download Full-text

Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-012-0048-9 ◽

2012 ◽

Vol 58 (4) ◽

pp. 351-356

Author(s):

Mincho B. Hadjiski ◽

Lyubka A. Doukovska ◽

Stefan L. Kojnov

Keyword(s):

Power Plant ◽

Trend Analysis ◽

Thermal Power Plant ◽

Power Plants ◽

Thermal Power ◽

Balkan Peninsula ◽

Predictive Maintenance ◽

Nonlinear Trend ◽

The Subject ◽

The Given

Abstract Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.

Download Full-text

Assessment of maintenance schedule uniformity for Belaz dump trucks in Chernogorsky open pit mine

MINING INFORMATIONAL AND ANALYTICAL BULLETIN ◽

10.25018/0236-1493-2018-12-64-127-133 ◽

2018 ◽

pp. 127-133

Author(s):

E. A. Vakulin ◽

V. A. Ivashkevich ◽

E. I.I. Gnitsak ◽

V. S. Baikin ◽

S. P. Maslyukov

Keyword(s):

Open Pit Mine ◽

Open Pit ◽

Time Loss ◽

Maintenance Schedule ◽

Monthly Average ◽

Dump Trucks ◽

Maintenance Personnel ◽

Productive Time ◽

Assessment Results

Uniform schedule maintenance of mining and haulage machines is one of the key conditions for increasing productive time of maintenance personnel and decreasing monthly average servicing time. Currently, Russian mines infringe regulated maintenance schedule aimed to improve output per shift. The loss of time of maintenance personnel and equipment as a consequence maintenance irregularity is never assessed. This article presents assessment results on maintenance schedule uniformity in terms of dump trucks BelAZ-7513 and BelAZ-7530 at Chernogorsky open pit mine, SUEK-Khakassia. A variant of calculation of time loss owing to inconsistent maintenance schedule for dump trucks is proposed. The loss of time by maintenance personnel and by mining/haulage machines is assessed. The fleet of dump trucks BelAZ-7513 and BelAZ-7530 is analyzed depending on overtime of operation between maintenance periods. It is recommended to improve uniformity of maintenance schedule for mining and haulage equipment.

Download Full-text