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@article{197727,
        author = {M Janani and V Karthikeyan and V Purusothaman and K Vishwanathan and R Selvakumar},
        title = {Machine Failure Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10856-10859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197727},
        abstract = {Industrial equipment performance control and failure prediction are important not just for the quality of the produced material, but also for the amount of time and money saved in overall maintenance. This project aims to monitor the evolution of AI/ML techniques for equipment fault prediction in industries over time. The topics covered in this paper include machine learning algorithms, use cases, and principles related to the application of such technology in a variety of industries such as software and hardware. This survey looks at early research from the late 1980s to the early 2000s, as well as recent research from the early 2000s to 2017, and the most recent research from the last two years. It can be inferred that this project offers a detailed review of various machine learning and artificial intelligence (ML/AI) approaches used in the Industrial Manufacturing domain. LSTM was discovered to be one of the most commonly used processes.},
        keywords = {},
        month = {April},
        }