Nederman Insight provides valuable data on how a filter is working and helps to build a broader understanding of the filtration system, its performance, maintenance needs, associated costs and potential improvements. Insight technology incorporates a series of sensors that monitor conditions in the filtration system. From the sensors, data is securely uploaded to the cloud via an Industry 4.0 gateway. This data can easily be read and interpreted via the Nederman Insight web-based user interface and dashboard.
What this capability gives the end user is round-the-clock access to real time and historical data that enables the optimisation of filtration systems and an understanding of how to utilise it fully. System performance is complemented by a risk management alert feature that informs the user when action is required to prevent extended downtime and keep the workplace safe.
The alert system ties in with maintenance schedules, while the access to historical data and the ongoing control of performance data will enable end users to plan maintenance needs. Nederman experts will help interpret the data and advise on settings, adjustments and product management strategies.
Improved awareness and maintenance of the filtration system will allow businesses to detect problems before they arise. By resolving issues or replacing spare parts in good time, customers can avoid unplanned stops and the associated costs of unscheduled downtime. With increasing demand on sustainability, Nederman Insight gives the user greater control of energy consumption, emissions and safety.
As an added benefit, the Nederman Insight ‘Action Centre’ will track filtration system performance and quickly identify issues, providing a full overview of filter operation via one or more dashboards with drill-down functions.
The company is continually evolving the platform by adding modules that give greater functionality and more valuable insights into air filtration systems. The latest example is a ‘machine learning’ smart algorithm that looks for patterns in data to help the user identify and mitigate risks. With machine learning, filtration systems will automatically improve from experience, without being reprogrammed.