equipment maintenance
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Logistics ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Sotiris P. Gayialis ◽  
Evripidis P. Kechagias ◽  
Grigorios D. Konstantakopoulos ◽  
Georgios A. Papadopoulos

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.


2022 ◽  
pp. 340-358
Author(s):  
Simon J. Preis

Predictive maintenance (PdM) is a key application of data analytics in semiconductor manufacturing. The optimization of equipment performance has been found to deliver significant revenue benefits, especially in the wafer fabrication process. This chapter addresses two main research objectives: first, to investigate the particular challenges and opportunities of implementing PdM for wafer fabrication equipment and, second, to identify the implications of PdM on key performance indicators in the wafer fabrication process. The research methodology is based on a detailed case study of a wafer fabrication facility and expert interviews. The findings indicate the potential benefits of PdM beyond improving equipment maintenance operations, and the chapter concludes that the quality of analytics models for PdM in wafer fabrication is critical, but this depends on challenging data preparation processes, per machine type. Without valid predictions, decision-making ability and benefits delivery will be limited.


2021 ◽  
Author(s):  
Francesco Beduschi ◽  
Fabio Turconi ◽  
Basso De Gregorio ◽  
Francesca Abbruzzese ◽  
Annagiulia Tiozzo ◽  
...  

Abstract This work highlights the development and results of a Rotating equipment predictive maintenance tool that allows to monitor the status of rotating machines through a synthetic "health index" and early detection of anomalies. The data-driven proposed solution is of great help to maintenance engineers, who, alongside the existing methodologies, can apply an effective tool based on artificial intelligence for early prevention of failures. Taking advantage of the high availability of remote sensors data, an anomaly detection machine learning model, which relies on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE), has been built. This model is capable of estimating, in real time, the health status of the machine, by matching the sensors actual values with the reference ones based on the Normal Operating Conditions (NOC) periods, that have been previously identified. If an anomalous behavior is detected, the Fault Isolation step of the model allows to evaluate which are the most contributing sensors for the investigated anomaly. These outcomes, combined with a failure mode matrix, which links the sensors deviations with the possible malfunctions, allows to highlight the most likely failure modes to be associated to the investigated anomaly. The developed predictive tool has been implemented on operating sites and it has demonstrated the capability to generate accurate warnings and detect anomalies to be processed by the maintenance engineers. These alerts may be aggregated into events in order to be monitored and analyzed by remote and on site specialists. The availability of alerts gives to the users the possibility to predict any deterioration of the machines or process fluctuations, that could lead to unplanned events with consequent mechanical breakdowns, production losses and flaring events. As a consequence, tailored operative adjustment to prevent critical events can be taken. Thanks to the tool, it is also possible to monitor over time the equipment behavior in order to provide suggestions for maintenance plans optimization and other useful statistics concerning the most recurrent failure. The tool's innovative feature is the ability to utilize the giant amount of data and to reproduce complex field phenomena by means of artificial intelligence. The proposed tool represents an innovative predictive approach for rotating equipment maintenance optimization.


2021 ◽  
Vol 2 (2) ◽  
pp. 97-107
Author(s):  
Ibnu Hakim ◽  
Sonki Prasetya ◽  
Aris Hendratmoko

Ban adalah salah satu komponen penting dalam pertambangan khususnya alat angkut/alat berat pada PT SBI. Guna memenuhi tuntutan produksi diperlukan jam kerja yang tinggi dari alat angkut, menyebabkan kinerja dari ban semakin berat dan berisiko untuk mengalami kerusakan. Kondisi yang terjadi saat ini dari tidak langsung terdatanya penggantian ban serta kurang efisien karena diperlukan dua tahapan dan dilakukan oleh orang yang berbeda dalam melakukan inspeksi harian dan bulanan sehingga memerlukan waktu lebih. Hal tersebut dapat menyebabkan peramalan menjadi tidak maksimal, akibatnya terjadi downtime karena menunggu part ban. Oleh karena itu solusi diberikan dengan membuat aplikasi sistem manajemen ban berbasis web yang dapat mempermudah memasukkan dan memonitoring data ban menggunakan database online. Diharapkan dengan digitalisasi ini dapat menciptakan efisiesi waktu dan efektif serta dengan jangkauan kerja yang luas. Proses ini akan dilakukan menggunakan aplikasi web yang telah dirancang menggunakan metode perancangan dengan UML, Laragon, dan pengembangan aplikasi menggunakan framework Laravel. Dengan aplikasi ini pekerjaan pendataan dan monitoring menjadi lebih cepat sebesar 99,4%, terjadinya paperless, lokasi akses yang luas, dan dapat mencegah risiko lost cost akibat downtime, Serta didapatkan hasil implementasi aplikasi beserta semua fiturnya yang sesuai harapan dengan hasil kuesioner adalah 80-100% atau sangat setuju dengan pertanyaan yang diajukan pada responden.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guozhen Zhang ◽  
Xiangang Cao ◽  
Mengyuan Zhang

With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.


2021 ◽  
Author(s):  
Yihao Li ◽  
Zhili Zhang ◽  
Xiangyang Li ◽  
Shiyin Guan

2021 ◽  
Author(s):  
Jinsheng Ma ◽  
Rui Wang ◽  
Jianhui Zhao ◽  
Xiaoning Wang ◽  
Tai Wang ◽  
...  

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