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@article{200450,
        author = {Vaishnavi Ganesh Bad and Shweta Manoj Shinde and Dr.T.B. Mohite Patil and Prajakta Tanaji Shinde and Samiksha Ankush Dalvi and Sheela Sachin Bangade},
        title = {Predictive Fault Diagnosis of Spindle Overload and Axis Overload in CNC Machine Tools Using Operational Data Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1045-1049},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200450},
        abstract = {Computer Numerical Control (CNC) machine tools are fundamental to modern manufacturing industries where uninterrupted operation and machining precision are essential for productivity. Among various failure conditions, spindle overload and axis overload are major causes of unexpected machine stoppage, job failure, and increased maintenance cost. Conventional monitoring systems generally rely on alarm-based detection that identifies faults only after abnormal conditions occur. This paper presents a study and conceptual framework for predictive fault diagnosis of overload conditions in CNC machine tools using operational data analytics. Industrial machine data obtained through machine drivers and stored in structured datasets are utilized to analyse load behaviour patterns. A data-driven methodology integrating preprocessing techniques and multiple machine learning models is proposed to enable early overload warning and maintenance prediction. The proposed framework focuses on improving machine reliability, minimizing downtime, and supporting intelligent maintenance decisions. Experimental validation using real industrial datasets is considered as future work.},
        keywords = {CNC Machine Tools, Predictive Maintenance, Spindle Overload, Axis Overload, Machine Learning, Industrial Data Analytics, Fault Diagnosis},
        month = {May},
        }