The unpredicted breakdowns in any industrial plant paves way for huge loss to the industry in terms of production and profit. If near future breakdowns are known well ahead of time, zero downtime can be achieved, maintaining the demand–supply chain which leads to industry-4.0 standard. This paper addresses the challenges involved in such transformation and proposes a monitoring and control procedure to reduce catastrophic breakdown by 84%. The parameters and signatures related to the breakdown phenomenon which are restricting zero down time in industry-3.0 are analysed. Adaptive ARIMA model based machine learning to support adaptive error prediction model through varying windowing technique is proposed to predict the future breakdown before its occurrence by forecasting the important signature parameters in the machinery. The proposed maintenance approach is implemented in a high-pressure hydraulic sand moulding machine in an automotive grey casting manufacturing foundry. In this work, the oil contamination level is the parameter identified for analysis in the high pressure sand moulding line in foundry. The breakdown in minutes per month and the number of breakdown occurrences in a month due to various phenomena are analysed after implementing the proposed approach. The proposed system ensures promising results which increases Mean Time Between-Failure by 800% and thereby achieving zero downtime in industries.

任何工厂的意外故障都将为该行业的生产和利润带来巨大损失。如果提前知道近期的故障，则可以实现零停机时间，从而维持了导致工业4.0标准的供需链。本文解决了这种转型所涉及的挑战，并提出了一种监视和控制程序，以将灾难性故障减少84％。分析了与故障现象相关的参数和特征，这些特征和特征在工业3.0中限制了零停机时间。提出了一种基于自适应ARIMA模型的机器学习技术，通过变化的开窗技术来支持自适应误差预测模型，以通过预测机器中的重要特征参数来预测未来的故障。拟议的维护方法在汽车灰铸件制造铸造厂的高压液压砂型成型机中实施。在这项工作中，油污染水平是在铸造厂的高压砂型生产线中确定用于分析的参数。在实施所提出的方法之后，将分析每月的分钟故障数和由于各种现象而导致的每月故障数。拟议的系统可确保有希望的结果，从而将平均故障间隔时间增加800％，从而实现行业零停机时间。油污染水平是在铸造厂的高压砂型生产线中确定用于分析的参数。在实施所提出的方法之后，将分析每月的分钟故障数和由于各种现象而导致的每月故障数。拟议的系统可确保有希望的结果，从而将平均故障间隔时间增加800％，从而实现行业零停机时间。油污染水平是在铸造厂的高压砂型生产线中确定用于分析的参数。在实施所提出的方法之后，将分析每月的分钟故障数和由于各种现象而导致的每月故障数。拟议的系统可确保有希望的结果，从而将平均故障间隔时间增加800％，从而实现行业零停机时间。