Online Fault-monitoring in Machine Tools Based on Energy Consumption Analysis and Non-invasive Data Acquisition for Improved Resource-efficiency

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

“…(2009) The machine tool model Exploratory Bengtsson and Kurdve (2016) Machining equipment life cycle costing model Modelling Westerkamp (2013) Optimization of machine tool Denkena et al. (2006) Optimization of machine design Proposed the development of an LCC navigator for analysis Lee and Suh (2008) Machine tools with product data model Modelling Emec et al. (2016) Use of diagnostic and prognostic online tool for fault monitoring and improved resource-efficiency Real time monitoring approach Zhao and Ming (2019) Optimization of a remanufacturing technology for motors with improvement in material sustainability.…”

“…A study conducted by Azkarate et al (2011) proposed specific tools and means to empower sustainable design of future machine tools, which will enhance sustainable manufacturing. Machine failure can shorten the remaining useful life of components and affect products' quality, the initial step is to achieve more effectiveness, which can be achieved by the incorporation of sensors for monitoring of machine availability and quality of machining processes (Emec et al, 2016). The incorporation of smart sensors in machine tools is the building block for the internet of things (IoTs) and data analytics in manufacturing which aids the collection of data in real time for process improvement and optimum performance.…”

“…The incorporation of smart sensors in machine tools is the building block for the internet of things (IoTs) and data analytics in manufacturing which aids the collection of data in real time for process improvement and optimum performance. A fault monitoring framework was developed by Emec et al (2016) for machine tools grounded on data stream mining techniques for online pattern matching in electrical power data streams. According to Relea et al (2019) non-stop exposure of oil or water based cutting fluids on granite machine tool beds could result in geometrical and dimensional variations in the machine's structure over a certain period.…”

“…The incorporation of smart sensors in machine tools is the building block for the internet of things (IoTs) and data analytics in manufacturing which aids the collection of data in real time for process improvement and optimum performance. A fault monitoring framework was developed by Emec et al. (2016) for machine tools grounded on data stream mining techniques for online pattern matching in electrical power data streams.…”

Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools

“…Using this algorithm, data in time series format can be accessed quickly and allows for use in IOT applications. To further extend this traditional CEP approach, Emec [9] modified the Algorithm by improving the initialization procedure and the unsupervised training. Their proposed framework uses hall-effect sensors for data acquisition which allows a flexible usage on a variety of different machine tools, independent of their specific interfaces.…”