SOSE
Soft-sensor methods in improving the competitivity of industrial products
Soft sensor or virtual sensor is a common name for software where several measurements are processed together. There may be dozens or even hundreds of measurements. The interaction of the signals can be used for calculating new quantities that need not be measured. Soft sensors are especially useful in data fusion, where measurements of different characteristics and dynamics are combined. It can be used for fault diagnostics as well as control applications.
Well-known software algorithms that can be seen as soft sensors include e.g. Kalman filters. More recent implementations of soft sensors use neural networks or fuzzy computing.
There are plenty of examples of soft sensor techniques:
- Kalman filters for estimating the location
- velocity estimators in electric motors
- estimating process data using self-organizing neural networks
- fuzzy computing in process control
The SOSE project develops soft sensor based software algorithms for integration with industrial maintenance and intelligent control products.
This project has the following subprojects: Harvester, VÄSY, VAIVI and PIHA.
Our research partners are Tampere University of Technology. Our research is financed by TEKES.
- Research area: Process Control, Mechatronics, Operations & Maintenance
- Related projects: TILLIKKA
- Publications (total: 35)
Subprojects
- Harvester - Intelligent maintenance and performance optimization of forest machines with soft-sensor methods
- VÄSY - Intelligent, Machine Vision Based Control for a Flotation Process
- VAIVI - Fault diagnostics of electrical AC machines
- PIHA - Circuit manufacturing control
Theses
Doctoral dissertations
- Sanna Pöyhönen: Support vector machine based classification in condition monitoring of induction motors (2004)
- Ander Tenno: Modelling and evaluation of valve-regulated lead-acid batteries (2004)
Licentiate Theses
- Mario Negrea: Numerical electromagnetic and thermal field analysis for fault diagnosis, condition monitoring and protection of electrical machines (2002)
- Sanna Pöyhönen: Support vector machines in fault diagnostics of electrical motors (2002)
Master's Theses
- Vesa Hölttä: Analysis of stem diameter measurement in forest harvester (2004)
- Kalle Kantola: Modelling of Electroless Nickel Plating Process for Control (2004)
- Martti Larinkari: Particle size distribution of crushed ore - measurement and management (2004)
- Jarmo Lehtonen: Neural network model-based fault diagnosis for induction motors (2004)
- Olli Ojala: Enchancement of X-ray analyzer measurements utilizing Deterministic-Stochastic Subspace Identification (2004)
- Tomi Lahti: Soft sensor -anturointiin perustuva kumin sekoitusprosessin viskositeettitason hallinta (2003, TUT)
- Lauri Palmroth: Adaptive stem grasping in tree delimbing (2002, TUT)
- Antti Huovinen: Model of electroless nickel plating process (2002)
- Pedro Rodriguez: Induction motor fault detection and diagnosis by monitoring the motor current (2002)
- Jani Kaartinen: Data Acquisition and Analysis System for Mineral Flotation (2001)
- Seppo Merikoski: Kumin sekoitusprosessin mallintaminen neuro-sumealla menetelmällä (2001)
Activities
