ORCID

Document Type

Article

Abstract

The rapid growth of Industrial IoT (IIoT) has led to large volumes of sensor data, increasing the need for scalable and data-driven maintenance solutions. This paper presents a replay-based streaming framework for predictive maintenance that integrates Apache Kafka for data ingestion, Apache Spark Streaming for distributed processing, and an LSTM-based model for fault prediction. The NASA CMAPSS FD001 dataset is used in a controlled streaming simulation to evaluate the feasibility of processing multivariate degradation data under different workload levels. To reduce data leakage, engine units are split before sliding-window sequence construction. Experimental results show that the LSTM model achieves 90.4% accuracy, 89.2% precision, 87.6% recall, and an F1-score of 88.4% on the test set. Under the controlled replay conditions, the observed post-Spark prediction-stage latency remained below 600 ms across the evaluated workload levels. Since the Kafka and Spark timing components and the original micro-batch interval were not retained separately, these measurements should not be interpreted as end-to-end pipeline latency. The results demonstrate the feasibility of integrating stream-processing technologies with deep learning for predictive maintenance, while full end-to-end latency and horizontal scalability remain subjects for future evaluation.

Publication Date

2026-10-06

Publication Title

Systems

Volume

14

Issue

10

Acceptance Date

2026-09-24

Deposit Date

2026-10-24

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

First Page

1

Last Page

23

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