For Industrial Intelligence, PSI relies on solutions that combine the reliability and robustness of industrial process knowledge with the entire spectrum of artificial intelligence (AI) methods.
The stability of the solutions is ensured by the industrially proven PSI software technology and the PSI framework. In total, PSI has supplied more than 50 different AI methods, which are permanently maintained and productively used.
Below you will find information on a selection of PSI solutions based on our many years of practical AI experience:
Machine learning and artificial neural networks are the basic technologies for forecasting loads and infeed by distributed energy resources. The results provide important input data for look-ahead calculations to determine the expected network state. Hence future challenges in network management can be safely and efficiently managed today.
For optimized production control of processes in the tire industry, artificial intelligence methods are used to learn the test and validation data required for automatic image recognition. The accuracy of the classification is over 99 percent. Manual interventions in the production process are reduced and quality is constantly assured.
The use of artificial neural networks optimizes automatic baggage detection based on high-resolution images from several surveillance cameras. High quality real-time detection and baggage tracking throughout the baggage handling process is guaranteed. For example, damage is automatically detected and logged.
In the process of value-adding process data analysis by Deep Qualicision, data relations for predictive control and optimization of business processes are machine-learned from historical data using Qualitative Labeling.
Extended fuzzy logic optimizes the key performance indicator-supported production processes with dynamic goal requirements and a certain and uncertain information situation. The advantages of optimization are, for example, cost savings through sequencing with flexible use of resources, the balancing of goal conflicts and the ability to react in real time.
Extended fuzzy logic is Industry 4.0-compatible and open for flexible control of processes far from rigid physical structures whether in areas like field/workforce management, predictive maintenance or agile production e.g. Automated Guided Vehicles for modular manufacturing with PSI swarm production.
AI real-time optimization combines the multi-criteria optimization requirements of line operation on the road from the perspective of passenger transport as well as from the economic perspective of the depot. In particular, the real-time aspect of optimization, which dynamically adapts itself to the current situation by analyzing the conflict of goals, constitutes the advantages of extended fuzzy logic as an AI method.
Extended fuzzy logic supports infrastructure operators in managing traffic flows and ensures optimum operation for all road users. Conflicts of objectives are balanced according to predefined key performance indicators. Maintenance procedures can be planned and smoothly integrated into the management of the transport networks.
Extended fuzzy logic optimizes storage and transport of goods. Conflicts of goals between different key performance indicators are recognized and balanced. Delivery costs are reduced and packaging logistics are simplified.
The SASO system makes forward-looking recommendations to eliminate current and expected malfunctions in the transport or distribution grid, which are evaluated on the basis of fuzzy logic and provide the basis for optimal decisions. SASO can thus be expanded into a self-learning system, a kind of power grid autopilot.
Extended fuzzy logic optimizes semi-automatically or fully automatically all processes in the depot of transport companies. This includes route optimization as well as vehicle positioning and tracking. The flexible configuration of all disposition criteria ensures improved vehicle maintenance while maintaining the required vehicle quality.
AI-based business process optimization uses qualitative labeling to evaluate machine data on a multi-criteria basis and recommends maintenance actions in advance. This increases machine availability, improves planning reliability, and reduces total operating costs.
Combinatorial processes optimize production and sequence planning in the steel and aluminum industry. The advantage of this procedure is the dissolution of competing goals in the sense of overarching strategies taking into account plant-specific specifications (e.g. maximum capacity utilization with simultaneous adherence to delivery dates). Predefined KPIs allow the necessary dynamics to react to changing requirements.
Various combinatorial processes support supply chain optimization, e.g. in the optimal selection of locations for logistics centers. Several criteria are taken into account and the most cost-effective solution is determined as a result. The processes are scalable and reusable.
Advanced industrial engineering processes can be used, for example, to predict and ensure the gas volumes required to fulfil gas supply contracts. Optimization processes in the operation of compressors and plants lead to cost savings and a reduction in CO2 emissions.
Advanced methodologies based on modern Machine Learning algorithms for the time series forecasting. The predictions of energy load and energy generation is now available with unprecedented accuracy due to the application of deep neural network and other machine learning methods. The high-quality forecasts provide real business benefits and clearly support the energy trade processes.
PSIgasguide identifies the best operational modes for reliable gas grid operations using boundary values and decision criteria. The software first utilizes future gas transport volumes and determines potential modes. A ranking is then calculated to assist the operator in selecting the optimal mode. The ranking is determined by evaluating different criteria based on a multi-criteria decision making technique.
Our customers already benefit - some for several years - from methods and processes of artificial intelligence in demanding industrial applications.
In combination with methods such as extended fuzzy logic, operations research and advanced industrial engineering, the limitations of individual processes are circumvented and high-performance solutions for industrial applications are developed.
The AI framework is based on the proven PSI technology platform.