Vitor Domingos’ Post

🌟 Excited to Share My Latest Project: Predicting Industrial Machine Downtime 🌟 In the ever-evolving world of manufacturing, minimizing machine downtime is critical for meeting production deadlines and ensuring operational efficiency. I recently completed a project where I delved into operational data from high-precision manufacturing machines, focusing on predictive maintenance and system optimization. 📊 Key Highlights: Analyzed over a year’s worth of machine performance data to uncover critical insights. Identified correlations between key parameters like spindle speed, cutting force, and hydraulic pressure. Highlighted actionable strategies to reduce downtime, including adaptive control systems and modular maintenance approaches. 💡 Insights: Shopfloor-L1 had the highest downtime events, signaling the need for targeted maintenance. Analyzed the relationship between cutting force and hydraulic pressure to suggest real-time monitoring systems. Proposed implementing machine learning algorithms for predictive maintenance. 📈 What I Learned: This project reinforced the value of data-driven decision-making in manufacturing and the power of predictive analytics to drive efficiency. It also highlighted the importance of clear storytelling in presenting data insights effectively. 🚀 Looking Ahead: I’m excited to further explore opportunities where data analytics and machine learning can revolutionize industrial operations! If you're interested in predictive maintenance, manufacturing analytics, or would like to collaborate on similar projects, feel free to connect or drop a message. #DataScience #ManufacturingAnalytics #PredictiveMaintenance #MachineLearning #Efficiency #Innovation

🔧 Sensor Component Failure Prediction Project 🚚 In predictive maintenance, reducing false predictions is essential to prevent unnecessary repair costs and, most importantly, avoid potentially hazardous breakdowns. Project Highlights: ➡️ Objective: Minimize false predictions to reduce repair costs and enhance safety. ➡️ Data Prep: Conducted extensive Exploratory Data Analysis (EDA) and used multiple imputation techniques (KNN, Simple Imputer - Median/Constant/Mean, MICE, PCA) to handle missing values. Tackled class imbalance with SMOTETomek to ensure our models are well-balanced. ➡️ Modeling: Implemented various machine learning models, including: Random Forest Decision Tree Logistic Regression K-Neighbors Classifier Gradient Boosting XGBoost CatBoost AdaBoost ➡️ Outcome: Among all models, XGBClassifier stood out, delivering a cost of $1440. False negatives (FN), which incur a higher cost of $500 per FN, contributed the most to the overall expense, compared to false positives at $10 per FP. Minimizing FNs became our focus to avoid costly truck breakdowns and ensure safety on the road. Conclusion: Leveraging advanced imputation, class balancing, and a diverse set of models allowed us to hone in on a solution that supports safer and more cost-effective operations. Predictive maintenance in action! For more details, check out the full project on GitHub: https://lnkd.in/gwvj8k_u #PredictiveMaintenance #MachineLearning #DataScience #XGBoost #Logistics #Safety #CostReduction #LinkedInProjects #MaintenanceTech

summarise and group_by functions from dplyr package are used in pairs to get deep insights from the data. The summarize function takes many observations and turns them into one observation. group_by function divides all the rows in the dataset into pre-defined groups. In this video we will generate some interesting observations on the automobiles data set by grouping the observations into three different groups and generating statistical summaries. You can watch the video here: https://lnkd.in/dNMaXDCi This video is about 5 mins duration... 😀

HAL APS - Production Planning and Forecasting Visit our website https://aps.host.pl/en Forecasting is the process of predicting future events, phenomena or outcomes based on the analysis of available data and information. The most common type of forecasting used in the systems is quantitative forecasting based on the analysis of historical data and statistics. Forecasts can be entered manually or supported by forecasting provided by more advanced mechanisms using, for example: A moving average that averages data from a specified period to smooth out fluctuations and obtain a forecast. Increasingly, machine learning uses linear regression to analyze the relationships between variables to predict future values. Another example is ARIMA autoregressive models that take into account seasonality and moving averages. HAL APS can be fed with an external forecast or entered manually. There are no limits on the length of the planning horizon that can roll over. Forecasts can be modified based on market information. HAL APS calculates the ATP Available to Promise indicator.

Enhancing the reliability of predictive modeling: https://ow.ly/pxvm50StkJL . . #predictiveanalytics #models #reliability #development

Time to Event Analysis: An Introduction https://lnkd.in/g6J9xVu9 A Statistical Methods for Failure-Time Data article by Shishir Rao. In this article, we will analyze vehicle shock absorber failure time data. Failure time data is also known as survival data, life data, event-time data or reliability data, depending on the field of study. and estimate a few basic survival quantities.

Bergen Engines to add ioCurrents analytics as optional extra https://lnkd.in/e3hnZRBu

Bergen Engines to add ioCurrents analytics as optional extra https://lnkd.in/eDCkEbGs

🗓Save the date: 2nd July, 3pm CEST ⚡Register NOW! 💡Learn more about the transformative role of AI technologies in the Supply Chain Finance industry. This will push SCF services to the next level Markus Schiffers Karel Krejčí, MBA Daria Thomas #Orbian #Accenture #Calculum #SupplyChainFinance #SCF #DigitalTransformation #AI #AITrends #TechInFinance #AIFinance #FinanceInnovation

I recently completed eCornell ‘s Understanding and Visualizing Data course as part of their Data Analytics certification program. Quality data being presented in an accurate easily understandable format is crucial to project success. I’m curious of how my fellow metrology connections take advantage of this when helping their customers. Feel free to start a conversation here or privately and we can help each other learn. #Boeing #SLS #Metrology #QualityEngineering