SysWatch

SysWatch Framework

SysWatch is a Windows program written in the Microsoft Visual Studio C# programming language running offline and online. By offline analysis we understand on the one hand the opportunity to visualize data but more importantly the training mode in which SysWatch reads a large, usually high-dimensional sensor time series database from a technical or industrial source, usually representing a physical process of some kind, eventually along with a database of historical events. The main objective in this mode is the establishment of correlations between the sensor readings and the events in question. In the online mode SysWatch uses computed and verified models for arriving data and calculated the learned outcomes.

Application Structure

The Explorer module is a professional visualization application of operational data. Furthermore it facilitates feeding technical data to the training step. A user-friendly GUI-Interface allows for a hierarchical structuring of all parameters. The Models module brings mathematical models into action. Models once generated are stored for analysis purposes. Besides the mathematical calculus a structured visualization and representation of KPIs is the main functionality of this module. The Dashboard visualizes the results of a Windows based service on the generated and active models. New data records are processed in the background and stored in MS SQL database.

Core Competence

The core competences of the framework lies in its algorithmic engine, including

  • Multivariate abnormality technology
  • Multivariate regressions
  • Multivariate pattern recognition
  • Empirical stochastic models
  • Advanced Semi-Markov process models
  • High-dimensional response surface generation

 

SysWatch has its origin in the predictive maintenance sector and is primary a monitoring software. Nevertheless in the course of time new fields occurred like our response surface analysis being applied in the smart operation field, as well as multivariate time series forecasting for energy demand or weather prognosis.

  • SysWatch "Monitor"
Flexibility in Data Source

Fault memory Analysis, Statistical Sensor Insights

Integration of System Know-How

Hierachical structuring, Structural Dependencies

Application of Predictive Algorithms

State of the art Algorithms, Embedding system-specific Features

Automation

Automated Data Stream, Real-Time Monitoring, Customized Dashboard Conceivable