The course aims to provide students with skills in: i) analyzing data using univariate, multivariate and high-dimensional statistics methods; ii) identification of anomalous situations; iii) analysis of heterogeneous data; iv) analysis of dynamic and non-stationary processes; v) time series prediction. At the end of the course the student will have to demonstrate that he is able to manage the monitoring of an industrial process. In particular, he will have to demonstrate that he is able to: i) identify potential failure modes; ii) design a data acquisition system on the production line; iii) identify anomalies in the process; iv) optimize the process parameters according to predefined objectives (rejection rate, time reduction, etc.); v) analyze the causes of unexpected failures (root cause analysis); vi) manage the maintenance of the system with predictive techniques.

Measurement and sensors:
- Foundamentals of industrial metrology: basic definitions, international system of units, measurement system model, errors, static and dynamic calibration
- Displacement measurement: resistive potentiometers, linear variable differential transformers, eddy current transducers, triangulation photodiodes, encoders, strain gauges
- Vibration measurement: vibrometers and accelerometers
- Flow measurement: pitot tube, hot-wire anemometer, pressure drop flowmeters, drag force flowmeter, ultrasonic flowmeter
- Thermal measurement: bimetallic thermometers, thermocouples, resistance temperature detectors, thermistors, bolometers and thermal imaging

Data analysis:
- Monitoring charts: Shewhart, cumulative sum, moving average, exponentially weighted moving average, Western Electric rules
- Univariate monitoring schemes: hypothesis testing, generalized likelihood ratio, Kullback-Leibler divergence, Hellinger distance, ordinary least square, ridge regression, principal component analysis and regression
- Multivariate monitoring schemes: multivariate monitoring charts, dynamic latent variable regression
- Unsupervised data analysis: hierarchical clustering, mean shift, k-Nearest neighbours, k-means, one-class SVM, support vector data description
- Fault isolation techniques

The exam involves the discussion of a project proposing a solution to an industrial problem.
The student will present his/her work in about 15 minutes (with or without the use of support material such as slides, written report, demo, etc.), followed by a Q&A session.
For the generation of the mark it will be taken into account:
- performance of the developed system (with different metrics depending on the problem);
- theoretical motivation behind the student's design choices;
- ability to clearly and concisely present the key points of the project;
- ability to support a discussion on possible alternative solutions and potential causes of failure of the solution developed.
The student must also demonstrate mastery of all the topics in the program (even those not addressed during the project).