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@article{195017,
        author = {Sakthi Rakesh g and sivaprakasam sk and santhosh v and prema v},
        title = {Machine failure risk prediction by multi modal deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7496-7501},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195017},
        abstract = {Industrial machine failures often lead to significant operational downtime and maintenance costs, yet traditional monitoring systems frequently struggle with the complexity of modern hardware. Conventional investigation methods primarily depend on unimodal analysis and manual threshold checks, which are often too slow to detect subtle degradation patterns before a critical breakdown occurs. With the increasing availability of high-frequency industrial IoT sensors and the progress made in Deep Learning, multi-modal data fusion has become a major game-changer for enhancing the accuracy of predictive maintenance,In this paper, we put forward MultiSenseNet, a multi-modal deep learning framework designed for advanced machine failure prediction. With this system, engineers can integrate heterogeneous data streams by fusing raw sensor signals, operational logs, and environmental parameters into a unified analytical pipeline. The system simultaneously captures complex temporal and nonlinear patterns, utilizing a robust architecture to identify failure signatures that traditional systems miss. Implemented in MATLAB, the framework streamlines the entire process from data preprocessing to performance analysis, significantly boosting early fault detection.},
        keywords = {Machine Failure Prediction, Multi-modal Fusion, Deep Learning, Predictive Maintenance, Sensor Data Analysis, MATLAB, Nonlinear Pattern Recognition, Industrial IoT.},
        month = {March},
        }