Summary
- Profile Type
- Technology offer
- POD Reference
- TOIT20231122018
- Term of Validity
- 22 November 2023 - 21 November 2025
- Company's Country
- Italy
- Type of partnership
- Commercial agreement with technical assistance
- Targeted Countries
- All countries
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General information
- Short Summary
- An Italian company offers a predictive maintenance solution for civil and industrial systems, based on artificial intelligence. The technology offered allows to provide a correct prediction of the failure time or residual life of the equipment, guaranteeing the optimization of the maintenance process and consequently the reduction of direct and indirect costs. The company is looking for partners to develop projects abroad.
- Full Description
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We live in a fully connected world where, both in industries and for final users, the presence of “smart” devices has a heavy influence on lifestyle and production processes. In particular, industrial systems and services are based on complex equipment connected to the network (Internet of Things), whose good operational status guarantees continuity and quality of production. Any interruptions in the production processes, due to failures that determine the need for extraordinary maintenance, represent significant company costs.
Today production plants’ maintenance is often preventive, based on planned interventions at precise time intervals, or reactive, based on extraordinary interventions when blocking malfunctions occur. These maintenance interventions are limited to adjusting the damage without storing the experience and information acquired to implement subsequent corrective actions in order to optimize the maintenance process. It is here that the importance of predictive maintenance is manifested.
Predictive maintenance has a high degree of applicability because real-time IoT solutions allow companies to acquire large volumes of information on the operation of the equipment, enabling the execution of predictive models able of reliably foreseen failures and detecting degradation of production processes. In this way it is possible to optimize maintenance activities, allowing a cheaper, more qualitative and more organized business process.
Predictive maintenance (PdM) uses advanced big data analytics techniques for IoT sensor data processing, enables decision-making and fault prediction during systems monitoring.
The solutions offered by the company also offer explanation functionality, that is, identification and interpretation of the root-cause of anomalies at the basis of the fault.
the strengths of the solution are:
Predictive Models
By applying descriptive analysis techniques, machine learning and deep learning (neural networks), it’s possible to recognize anomalous behaviors (outlier detection) and / or to predict the residual useful life time of the equipment
Automated Reasoning
Thanks to reasoning engines based on Answer Set Programming it’s possible to enable the execution of logical rules for decision-support of end users
Explanation
Failures multidimensional analysis are used to highlight the malfunctions’ causes, allowing critical sub-systems repair with time savings and more efficiency
Big Data acquisition and storage
Sensor data acquisition requires robust and extensible IoT gateways, and Big Data storage is based on NoSql allowing time series management
Cloud Platform
Storage, processing and explanation features are made available in the cloud.
The platform could also be installed on-premises
Edge computing
Failure prediction models can be executed “on board”, minimizing reaction times and reducing the time and costs for transferring large amounts of data to the cloud - Advantages and Innovations
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A correct forecast of the failure-time or of the residual equipment life-time ensures maintenance process optimizations (i.e. for workforce scheduling, warehouse management, plant downtime minimization) and consequently in reducing direct and indirect costs. The solution offered by the company presents a high degree of innovation thanks to the basic technologies used:
Big Data
Big Data is a large quantities of intermittent data streams (in real time or batch) from different data sources, mainly time series, which have different schemas and data structures
Artificial Intelligence Platform
an extensible and scalable artificial intelligence framework that can be easily integrated into customers’ operational contexts. Rialto™ allows the analysis/monitoring of large amounts of data, the forecasting of phenomena and the explanation of decision models
Machine/ Deep Learning
Supervised and unsupervised approaches, in addition to the use of Neural Networks (e.g. Recurrent Neural Networks)
Automated Reasoning
Answer Set Programming techniques, Ontologies and of Disjunctive Logic Programming - Stage of Development
- Already on the market
- Sustainable Development Goals
- Goal 9: Industry, Innovation and Infrastructure
- IPR status
- Secret know-how
Partner Sought
- Expected Role of a Partner
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Type
IT companies operating in the civil or industrial sector
Companies that intend to activate innovation processes in their civil or industrial plants
Role
The Italian company is looking for both technological partners for the development of joint projects and companies that intend to bring innovation to their plants or to plants managed by them - Type and Size of Partner
- SME 11-49
- SME 50 - 249
- Big company
- SME <=10
- Type of partnership
- Commercial agreement with technical assistance
Dissemination
- Technology keywords
- 01003013 - Information Technology/Informatics
- 01003003 - Artificial Intelligence (AI)
- 01003024 - Cloud Technologies
- 01003008 - Data Processing / Data Interchange, Middleware
- 01003025 - Internet of Things
- Market keywords
- 02007001 - Systems software
- 02007011 - Manufacturing/industrial software
- 02007007 - Applications software
- 02007016 - Artificial intelligence related software
- 08005 - Other Industrial Products (not elsewhere classified)
- Sector Groups Involved
- Digital
- Targeted countries
- All countries