A Smart Predictive Maintenance Scheme for Classifying Diagnostic and Prognostic Statuses
| Autor: | Revi Asprila Palembiya, Elnora Oktaviyani Gultom, Nani Kurniati, Muhammad Nanda Setiawan, Mohammad Iqbal, Adila Sekarratri Dwi Prayitno |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Ground truth Computer science business.industry Deep learning Binary number Machine learning computer.software_genre Predictive maintenance Annotation Feature (computer vision) Artificial intelligence Baseline (configuration management) business computer computer.programming_language |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811673337 |
| DOI: | 10.1007/978-981-16-7334-4_8 |
| Popis: | This study attempts to propose a smart predictive maintenance method to classify manufacturing machines’ diagnostic and prognostic statuses. The main goal of this study is to reduce the manual predictive maintenance budgets of manufactures in Indonesia. In the proposed method, we perform feature maps to obtain the binary states of sensor data, which is further clustered into the machine’s error states (diagnostic status) and the machine’ useful life states (prognostic status). Moreover, the proposed method comprises the two states predictions of machines based on Deep Long Short Term Memory. The proposed method is demonstrated on the Rawmill and Kiln machines of a cement factory in Indonesia for evaluation performances. Without labelling manually, we investigated the annotation of both states, which are similar to the ground truth. In addition, the proposed method can achieved high accuracy and outperformed to another baseline method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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