Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories

A real-time adaptive simulator with high fidelity will be a demonstrator (remote or local) of the machine’s state, in which a fast-forward simulation mechanism (prognostic models) predicts the potential events of breakdown of components and machines. What-if capabilities will allow the maintenance planners to find the most effective and cost-efficient schedules for component replacement and maintenance plans.

Z-BRE4K solution will guarantee the improved product quality since the machines will be stopped and the maintenance will be carried out before any failure occurs and defective products are manufactured.

By reducing failures, downtimes, unplanned outages the manufacturing will minimize the lead time and maximise the response time: stock-outs, lot processing delays, capacity bottlenecks will be avoided.

Once the Z-BRE4K system has evaluated an anomaly or a deterioration trend, the maintenance scheduling is optimized. Moreover, manufacturing machinery execution parameters can be adapted so that the remaining useful life of the incumbent system can be improved, providing Operations Managers with a flexible shopfloor.

One of the main advantages of Z-BRE4K is the Z-SYNCHRONIZE strategy that enhances the coordination of production planning, logistics and maintenance operations.

The minimization of downtimes and failures, the optimization of maintenance operations and the reduction of row material waste will lead to an increase the in-service efficiency by 26% and increased productivity.

The avoidance of defective production and overproduction will lead to a better efficiency in the use of materials.

Z-Bre4k will contribute to the optimisation of the manufacturing processesresulting in significantly less waste and scrap. Z-Bre4k will contribute to the reduction of defective production thanks to the optimisation of manufacturing through model-based control and improved accuracy. Moreover, it will allow to avoid overproduction that is to say manufacturing items for which there are no orders thanks to the collection of data that will control the production process producing only what is required and not overproduce.

A dedicated Z-SAFETY strategy will look for the drastic reduction of accidents.

Z-BRE4K solution provides a big data analytics framework for the identification of the deterioration trends to extended towards prescriptive maintenance. Advanced data analysis tools are under development, to be applied to the quality and production data to realise zero-defect and zero-break down production. Furthermore, it involves models for anomaly detection, that are capable of identifying the machine states where the operation deviated from the norm.  This is achieved by collecting the data from the machine sensors in chunks of time and processing them in batch through deep learning models. The models are trying to recreate their inputs, and this results in an observable measure called Reconstruction Error, which is used to identify states that the models aren’t capable of addressing sufficiently (which constitutes an anomaly.

In the framework of Z-BRE4K, an IoT approach is applied to integrate end user machines to the Z-BRE4K platform. Through IoT gateways deployed at the shop floor, machine components are enabled to communicate their conditions, sending sensors data to the cloud where they are stored and analysed to provide predictive maintenance related information.

The UI’s goal is to visualize data from maintainers and components statuses (real time data and relevant KPIs). The SPARQL Web service is used to send custom SPARQL queries against the Semantic Framework RDF repository as a general-purpose querying web service. The UI can visualise the probability of breakdown and RUL. CAD and CAE models would be useful in mapping these values into a 3D visualisation.

The AUTOWARE apps development will be supported and linked to the FIWARE. AUTOWARE approach is to connect and extend the FIWARE for Industry resources and assets to the end of digital automation community, so synergies emerge both in terms of multi-sided business opportunities and amount of resources that are made available to the cognitive automation community to build their autonomous solutions and apps.

The choice of the AUTOWARE platform was based on the fact that is an open source project, and that is hardware agnostic.

Z-BRE4K will contribute to the productivity increase of different critical manufacturing processes, such as joining (GESTAMP), cutting (PHILIPS) and forming (GESTAMP, PHILIPS, SACMI) by providing analytics and suggestion in to order to assist in minimizing the machines breakdowns. The main gain is operational and maintenance costs reduction. Furthermore, for the GESTAMP use case a real-time arc welding quality control system, based on infrared images, is being developed

Part of the Z-BRE4K project is the development, of a novel embedded condition monitoring solution with cognitive capabilities, by applying deep learning techniques to reduce the dimensionality of multimodal sensor data associated to a given machine/device, and provide meaningful features to predictive maintenance services on the cloud. Most suitable IoT edge devices, for optimal trade-off between computational power and energy consumption, sensors, providing relevant information of the condition of different components, and signal processing algorithm are proposed for different machines and processes. Data gathering is enabled by the installation of IoT gateways, where data in different protocols are homogenised and sent to the cloud for storage. Real-time data, relevant KPIs and information about components status are visualised through dedicated dashboards.

The modelling and simulation methods used in Z-BRE4K are mainly Finite Element Methods (FEM) where complex problems and processes from the real world are being simplified and solved using a numerical approach. First, an accurate digital model of the geometry and material properties of all involved objects, boundary conditions between these objects and process data is created (i.e. forces or temperature). 

Then, the complex shape of all objects involved, is approximate using a finite number of simple geometries (i.e. triangles) which simplify the complex mathematical problem. A computer is capable of solving these mathematical operations at a rate impossible for humans and thus enables the user to analyse various scenarios, ranging from mechanical strains within the objects to rise in temperature or material fatigue. This information can be used to predict the remaining useful lifetime of a given tool.

Simulation platform is deployed by the physical equipment to create intuitive maintenance control and management systems. The Z-BRE4K’s platform simulation capabilities will estimate the remaining useful life calling for maintenance and suggesting the optimal times to place orders for spare parts, reducing the related costs. The increased predictability of the system and the failure prevention actions will reduce the number of failures, maximise the performance, reduce the repair/recover times reducing further the costs.

By applying time series analysis, we are able to detect special events that are known (Fault detection) or unknown (anomaly detection) during production. This information, correlated with sensor readings is fed into machine learning algorithms that create estimates of Remaining Useful Life (RUL), Health Indexes (HI) and forecast upcoming events (Likelihood of Failure). Special focus is given in techniques that can provide real-time information (Fast computation and high accuracy) as well as being scalable in order to use new data as it becomes available. Additional information such as meantime between failures based on historical data or an expert opinion, CAE data, quality control data, real time states etc. are also used to the design of machine simulators.

Orion is a C++ implementation of the NGSIv2 REST API binding developed as a part of the FIWARE platform that allows the management of the entire lifecycle of context information including updates, queries, registrations and subscriptions. It is an NGSIv2 server implementation to manage context information and its availability allowing subscription to context information so when some condition occurs notifications are sent. The Industrial Data Space foster secure data exchange among its participants, while at the same time ensuring data sovereignty for the participating data owners. The architecture of the Industrial Data Space does not require central data storage capabilities but follows a decentralized approach, meaning that data physically remain with the respective data owner until they are transmitted to a trusted party. Thus, the Industrial Data Space is not a cloud platform, but an architectural approach to connect various, different platforms.

The Z-Bre4k solution is based on the blackboard architectural model. This model is mainly an artificial intelligence approach, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. The blackboard component acts as a central repository system. The rest of the software applications (components) act independently at the common data structure stored on the blackboard, they respond on changes and create new reactions according to changes. Interaction between components is implemented via the blackboard.

Z-BRE4K will provide a modular solution for predictive maintenance that is highly customizable. Therefore, the different modules of the solution cane be sold as stand alone products or can be combined depending on the users needs. Z-BRE4K solution can be applied to both new machines and old machines that were not designed to be equipped with predictive maintenance solutions.