Data analytics

Data analytics

1. Data come from different areas, such as product and process design, assembly, material planning, quality control, scheduling, maintenance, fault detection and cover all the product life cycle phase.

   • MAIN CONCEPTS
     • Data mining: is the extraction of implicit, previously unknown and potentially useful information from data
       • Machine learning: is the field that developed most of the algorithms to find and describe structural patterns in data.
     • Data analytics, or equivalently data analysis: is a trend word, since it is strictly connected with the buzzword “Big Data
     • BIG DATA
       • Big Data refer to a type of datasets, in which variables are different in type and nature, for this reason, they compel to be analysed with a number of specific techniques, models and algorithms, that belong to the Data Analytics, Data Mining and Machine Learning.
         • EXTRATION OF VALUE FROM DATA
           • The extraction of value from data is a new challenge that companies are now experiencing. Consequently, the need for analytical information systems is ever-growing to guide corporate decision-making, during the different phases of the product process, such as production, maintenance, logistic and quality control.

2. Data analytics wants to analyse raw data in order to discover hidden patterns and relationships among different variables.

   • to understand how to capture data along the production process, how to analyse data in order to extract useful knowledge about product, production process and product life cycle; how to share information among all the different stakeholders, along all the product life cycle

3. MACHINE LEARNING TECHNIQUES IN SMART MANUFACTURING

   • Linear Methods for Regression: Linear models for regression were largely developed in the pre-computer age of statistics, but even today there are still good reasons to study and use them
   • Linear Method for Classification :The linear classifier uses the variable’s characteristics to identify which class (or group) the variable belongs to
   • Decision Tree Based Method: A “divide-and-conquer” approach to the problem of learning from a set of independent instances leads naturally to a style of representation called a decision tree
   • Neural Networks :The central idea is to extract linear combinations of the inputs as new features, and then model the target output as a nonlinear function of these features
   • Principal Component Analysis Principal Components Analysis (PCA) . PCA converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.
   • Association Rules Association rules emerged as a tool for mining commercial databases. The goal is to find joint values of the input variables that appear most frequently in the dataset, i.e. to predict combinations of attributes.
   • Cluster Analysis: is used to recognize whether the dataset consists of distinct subgroups, each group representing objects with substantially different properties
   • Self-Organizing Maps : A SOM map consists of components called nodes or neurons. Each node is associated with a weight vector of the same dimension as the input data vectors and a position in the map space