/ AI services for better performance
/ Our Features
- Performance monitoring
- Anomaly detection
- Data insights
- Event management
- Visualized real-time data
- Using physic based models to detect changes
- Combination of operator & machinery feedback
- Annotation & aggregation on events
- AI events & various plots
- Model fitting on real-time or historic data
/ More than just energy management
Our energy management enables to track not only energy consumption but also enables the deep details about the main consumers and usages within the industrial plant.
With the ability to locate the most energy consuming parts of a machine, operators are gaining high accuracy and deep machinery insights empowering better decision-making for the setup and the operation of the machinery. The AI algorithm will not help to detect and uncover efficiency potentials, but also give recommendation on what to do next.
- Cloud or on-premise
- Data integration into OsiSoft, SAP, ABB etc.
- Notifications via e-mail or MS Teams
- Data connectivity via OPC UA or other secured systems
- Requirements data sheets, P&IDs
AI Event Dashboard
Manage your events
- Annotate & label events
- AI learns from operator feedback
- Receive event notifications
Simulate your plant
- Physical based models
- Calculating of soft sensors
- P&ID view of equipment
IoT enabled Vibration Monitoring
- Preprocessing of data
- Connect sensors & PLCs without IoT
- Flexible & cost-effective hardware
Dive deeper into our technology
- Model fitting
- Edge Analytics
- Evolutionary Algorithms
- Anomaly Detection using First Principle Models
- Vibration analysis
Industrial Analytics Thermodynamic Process Models are designed to be fitted without geometric data from the equipment, instead it uses measured data of the process.
Thermodynamic & Rotordynamic Process Models
A key necessity are the gas properties that are taken from an efficient implementation of the NIST REFPROP model. They provide additional insights of the monitored equipment and feature reasonable extrapolation into regions where the model is not trained.
The signal analysis software is developed by Industrial Analytics. It is possible to extract features relative to the rotational speed of the machine and a broad representation of the spectrum.
Transmitting the data
The data rates are reduced using a highly effective data compression algorithm. The data is transmitted via secure standard protocols like OPC-UA or can be fed directly into a PI System by OSIsoft Message Format. It is possible to use wired or Wi-Fi connections.
Explainable AI models give not only the best answer, but obtain statistical information on the accuracy. The anomaly detection uses ensemble learning with training of multiple models. The results are a better estimation of expected value. This expected value is compared with the actual measured value.
Maximizing extrapolation capabilities while minimizing uncertainties
Industrial Analytics uses a reliable way to estimate the uncertainty of the model and the data. Maximizing Bayesian model evidence results in maximizing extrapolation capabilities while minimizing uncertainties. Generation of indicative events is based on hypothesis testing, which uses a quantifying effect size and probability of the errors and an automatic adjustment of the evaluation period.
The general monitoring strategy is to detect changes of the equipment relative to a state that is defined healthy. With this strategy, it is possible to monitor equipment without witnessing faults.
Accessible useful data
In many cases, the accessible, useful data do not have enough faults to use classic predictive maintenance strategies that rely on a degradation model.
We use already trained models for certain physical assets, such a steam turbines or turbo compressors. So our customers don’t need extra data scientists for the fitting.
Vibration signatures are the most sensitive indicator for machinery health. Any defects are typically first indicated by a change of the vibration spectrum.
Cost-effective edge device
Industrial Analytics has engineered a cost-effective edge device which is built from industrial approved components. It is possible to use accelerometers or typical displacement sensors for shafts vibration. Other additional sensors can be added to the highly modular system.
Data modelling & AI training
/ What's in for you?
We are experts in engineering and data science. We want our clients to understand their machinery better and optimized their production.
Reduction of unplanned downtime
Support of operator activities
Saving on maintenance costs
/ What our clients say
Security of continuous supply is the highest priority for us. With the help of Industrial Analytics, we can use the additional knowledge about our machines to flexibly schedule maintenance measures and save costs.
Our goal is to secure our competitiveness with the help of innovative, digital services and to further extend our lead. The solution from Industrial Analytics increases operational reliability and transparency in the maintenance process.
For us, the potential of digitization is on the one hand the automation of work steps, but above all the preservation of (specialist) knowledge. Industrial Analytics supports us in system monitoring, so we can optimize the production process and reduce our maintenance costs.