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Solution

Planning & Prediction

z = −1 + i' nα = θ + 2πk u = ρ (cosα + isinα) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i nα = θ + 2πk

Planning & Prediction

Predictive Maintenance

More and more companies are collecting massive amounts of data to monitor the health of their products or processes, with the expectation that they should always be operating at peak performance.
Being able to recognize operating drifts and anticipate degradation and possible failures through data, is of paramount importance in order not to be left unprepared in the face of various unforeseen events that may occur (e.g. downtime, quality problems, or customers in trouble). It is also crucial for optimizing business planning and improving consumers' perception of the brand.

z = −1 + i' nα = θ + 2πk u = ρ (cosα + isinα) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i nα = θ + 2πk

Thanks to our solution, Predictive Maintenance, it is possible to improve the planning of maintenance activities, limit the impact on production and enhance the service offered to customers.
The solution provides a dashboard showing the list of upcoming faults, highlighting the associated components and machines. Moreover, the user can perform an in depth interpretation and investigation of the causes of the possible failure, according to a predictive diagnostics logic.

Always-on machinery

Thanks to continuous and automatic health monitoring

Increased sustainability

The prediction of maintenance allows to minimize activities, have more time to have well considered selection of spare parts and reduce trips for their procurement

Costs and margins control

Thanks to the zero downtime, production will always be at its peak

Examples

Sectors

Application examples of Predictive Maintenace

Industrial

Data-driven failure prediction to minimize downtime and plan necessary interventions

Transportation

Predict vehicle failures and malfunctions and improve infrastructure maintenance

Energy

Predict hydroelectric power plant failures to avoid service interruptions

Deepening

Operating logic

In order to develop this solution, we have used an advanced machine learning algorithm for supervised learning that allows to provide an estimate of the remaining life (Remaining Useful Life) of the observed components.
This algorithm was chosen both for its effectiveness in handling complex and heterogeneous data and for its high predictive performance.
The unsupervised techniques, based on functional data analysis, make the solution completely data-driven, very flexible and easily scalable even in complex contexts.