The SysWatch framework is a monitoring solution in the area of predictive maintenance. It is a data driven condition based monitoring system using different methodologies in order to provide reliable early warnings of deteriorating asset conditions. More and more sensors capture the state of modern systems periodically, whereby a complete coverage of stochastic dependencies is a must. Employing the routinely recorded sensor data, it generates an added value by detecting anomalies and predicting critical events in a system in order to avoid expensive, catastrophic machinery malfunctions and reduce the risk of unplanned down-time and costly repairs. The prevention of malfunctions
- Improves security
- Increases availability
- Reduces maintenance costs
- Protects the image of an organization
This prevention results from a delicate balancing procedure, trying to predict as many critical events as early as possible, while at the same time avoiding false alerts. SysWatch consistently handles this challenge as a solution to a complex multidimensional problem.
Like all pattern recognition work, these correlations will enable the program, to be applied to the sensor readings and detect or even predict the events in question.
As simple as this task sounds, as complicated it is. Some of the main challenges are the following:
- Does the entirety of sensor readings have enough predictive power to predict the events ?
- What are the optimal values of the parameters to minimize Type I and Type II errors simultaneously?
- Are there any significant influence variables missing
SysWatch is equipped with a powerful set of data analysis tools to decide the above questions. Data preprocessing steps such as correlation analysis, sensor grouping etc. will be activated before any evaluation is performed. We are convinced that SysWatch can enhance your existing monitoring capabilities and at the same time improve productivity metrics while lowering operating cost.
The following video tutorials give insights in functionalities and software handling.