In the context of intelligent manufacturing, this paper conducts a series of experimental studies on the predictive maintenance of industrial milling machine equipment based on the AI4I 2020 dataset. This paper proposes a complete predictive maintenance experimental process combining artificial intelligence technology, including six main links: data preprocessing, model training, model evaluation, model selection, SHAP analysis, and result visualization. By comparing and analyzing the performance of eight machine learning models, it is found that integrated learning methods such as XGBoost and random forest perform well in milling machine fault prediction tasks. In addition, with the help of SHAP analysis technology, the influence mechanism of different features on equipment failure is deeply revealed, among which processing temperature, torque and speed are the key factors affecting failure. This study combines artificial intelligence and manufacturing technology, provides a methodological reference for predictive maintenance practice in an intelligent manufacturing environment, and has practical significance for promoting the digital transformation of the manufacturing industry, improving production efficiency and reducing maintenance costs.

Artificial Intelligence (AI) has been tainted with a lot of negative press since its conception. Many people in the workforce have started to question the motives for implementing AI in factories and other workplaces. However, there are responsible AI practices aimed at making the workplace a better place for employees. A prime example of this includes the applications of AI in manufacturing. Its implementation in this industry proves that the tech is only good as its user.

"How can I solve my problem with AI?"- As Machine Learning and Artificial Intelligence reach more and more areas of daily life and enter all economic sectors, this question is often asked by decision makers eager to integrate AI into their business. While AI can offer great gains to businesses, in the following, you will see why jumping in with such a question is not an appropriate approach. Before diving into AI for your business problem, a well-defined business strategy must be established and the question of "Why should I use Machine Learning/Artificial Intelligence?" should be thoroughly considered. Being able to answer that question requires having the exact definition of the business problem: knowing the available data and desired output, having a plan for testing, monitoring and improving your solution, and being clear about the end use-case. After all, it's no use having a perfectly designed model from the data science team if you haven't planned how the rest of the company can use your model outputs.

Senior Technical Fellow PeopleTec, Inc. Huntsville, AL, USA ABSTRACT For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements and sensor inputs. To simplify the timeaccuracy comparisonbetween 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. We identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. Because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times. INTRODUCTION Successful predictive maintenance is challenging not only because failures can prove multifactorial but also because maintenance forecasters often lack good training data.

When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score.