Hendri Hendri, Miftah Farid Adiwisastra, Yani Sri Mulyani

Comparison of Chronos and Conventional Models: Predicting Machine Downtime using Time Series

Introduction

Comparison of chronos and conventional models: predicting machine downtime using time series. Compare Chronos & conventional models for industrial machine downtime prediction using time series. Discover how XGBoost outperforms advanced AI like Chronos on small datasets, boosting efficiency.

Abstract

This study analyzes the comparison between a pretrained transformer model (Chronos) and conventional models in predicting industrial machine downtime using time series data to achieve greater accuracy and efficiency for companies. More specifically, this research focuses on early detection before downtime occurs to reduce company losses in terms of both costs and product quality, and to ensure that Key Performance Indicator targets are met. Design/methodology/approach: The research methodology includes primary data collection, data preprocessing, and sequential data splitting (80% training, 10% validation, 10% testing) to prevent potential data leakage. Model evaluation is measured using the Mean Absolute Error loss function, focusing on the “handling machine” category, which yields 4,069 to 4,101 data rows after the preprocessing stage. Research showed that the conventional XGBoost model with tuning performed best, with the lowest Mean Absolute Error among the other models. XGBoost proved to be highly effective and was capable of outperforming advanced transformer-based models (such as Chronos), particularly when applied to a limited dataset of 4,069 data points. Conversely, transformer architectures like Chronos performed poorly on small datasets because they were designed for massive datasets. This study focuses on the application and evaluation of modern artificial intelligence technologies, specifically transformer architectures such as the Chronos model. Although previous similar studies have successfully predicted downtime accurately using conventional models (such as ARIMA, Random Forest, Support Vector Machine, and autoencoders), those earlier studies have not tested the effectiveness of transformer architectures in detecting machine downtime.

Review

This study presents a timely and relevant comparison between a state-of-the-art transformer model, Chronos, and conventional machine learning approaches for predicting industrial machine downtime. The objective of improving accuracy and efficiency for early detection, thereby reducing company losses and ensuring KPI attainment, is highly commendable and addresses a critical industrial need. The most striking and significant finding is the superior performance of the tuned conventional XGBoost model over Chronos, especially when applied to a relatively limited dataset. This challenges the common assumption that more complex, advanced architectures like transformers will inherently outperform simpler models, particularly in real-world industrial scenarios with data constraints. The research successfully pioneers the evaluation of transformer architectures in this specific domain, adding a novel dimension to existing literature. While the study's methodology includes crucial steps like primary data collection, preprocessing, and sequential data splitting to prevent leakage, the abstract could benefit from more detailed information regarding the dataset itself. Specifics on the nature of the time series data (e.g., sampling frequency, duration covered by 4,069-4,101 rows, types of features included beyond "handling machine" category) would significantly enhance the interpretability and generalizability of the results. The explanation that Chronos performed poorly due to its design for massive datasets is plausible and an important takeaway, but it also suggests that the comparison might be inherently skewed if Chronos was not operating under its optimal conditions. Clarification on which specific conventional models beyond XGBoost were part of the head-to-head comparison would also strengthen the narrative, as the abstract mentions them broadly but highlights only XGBoost as the top performer. The findings carry substantial practical implications, particularly for industries that may not possess "massive datasets" but still require robust predictive models for downtime. It strongly suggests that investing in careful feature engineering and tuning established models like XGBoost can yield exceptional results, even outperforming advanced AI. Future research could extend this valuable work by exploring the performance of Chronos and similar transformer models on significantly larger and more diverse industrial datasets, potentially from different machine categories or industries, to fully understand their capabilities in data-rich environments. Additionally, investigating data augmentation techniques for smaller industrial datasets to potentially unlock the benefits of transformer architectures, or exploring hybrid models that combine strengths of both conventional and deep learning approaches, would be valuable next steps.

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