Protecting your data is a top priority for aiomatic. We are currently in the ISO 27001 certification process and use proven technologies to ensure the security of your data. These include TLS encryption, use of the Azure Cloud and IP whitelisting.

ai-omatics technology differs significantly from the solutions of competing companies in terms of its scalability and the accuracy of the results. To do this, the digital maintenance assistant developed by ai-omatic uses the engineer's specialist knowledge of the causal dependency structure between machine variables and combines this domain knowledge with the power of machine learning. This allows for higher model accuracy and more explainable results, increasing the acceptance of the approach among engineers. The result: - 20% better prediction accuracy - 70% faster scalability - an explainable AI solution for predictive maintenance

The customer receives access to a web-based dashboard on which the health score of the machine is displayed in the form of a factor between 0% and 100%. Through this, ai-omatics customers get an insight into the condition of their machines and can trace abnormal machine behavior down to the sensor level. By means of a trend display, the dashboard also offers the user the option of evaluating the forecasts and assessing value groups.

Use of existing data: ai-omatic uses data that is already collected from the machine or the process. This means that no additional sensors are required. This significantly reduces costs and installation effort. Taking into account various data points: In contrast to pure vibration monitoring, the maintenance assistant can include data points that have already been recorded. This makes it possible to monitor machines where vibration patterns do not provide meaningful information or where components within complex machines are difficult to access. Inclusion of external influencing factors: The biggest difference is that the ai-omatic maintenance assistant includes external influencing factors, such as different operating modes or the current product recipe, in the analysis. Unlike sensors that are not synchronized with the process, ai-omatic can more closely monitor the "normal state" of the machine. This enables a more precise detection of changes and an early indication of possible problems. ai-omatic learns individual vibration patterns for each distinguishable operating mode, which ensures high sensitivity and accuracy.

aiomatic is able to process different types of data as long as they are available via a suitable interface (especially OPC UA). This includes numbers, decimals, text, and true/false values. aiomatics software takes into account both sensor data and the expertise of the engineer, which can be entered via a simple interface. By combining these two types of data it is possible to provide effective maintenance.

aiomatic is able to monitor the behavior of machines and detect even the slightest deviations in the data. Taking the contextual factors into account, a risk assessment of the abnormal machine behavior can be made, alerting users to adverse machine behavior. This ensures, among other things, the following: - Early detection of machine or individual component failures - Identifying the causes of the erroneous behavior - Fast troubleshooting and avoidance of consequential damage and costs

Access to machine data can be enabled through several solutions. Typically, data is queried at the machine using the OPC UA protocol. Standard software solution: ai-omatic software for retrieving data from the machine and transferring it to the AI-Omatic Azure Cloud. IoT gateway: Aggregates various data sources, ideal for retrofitting vibration sensors. Supports cellular transmission to the ai-omatic Azure Cloud. Siemens Industrial Edge device: Communicates via software modules with all common industrial protocols and transfers data to the ai-omatic Azure Cloud. MQTT-based custom solution: Allows sending machine data from existing software in a predefined format to an encrypted MQTT broker in the ai-omatic Azure Cloud.

aiomatic's algorithm does not require any historical sensor data or error logs, but learns from new data that is sent to the system during live operation.

To utilize the ai-omatic software, specific hardware requirements must be met. On the customer's side, an Ubuntu server with access to the OPC UA server should be available. The server can be implemented as either a physical server or a virtual machine (VM). For a VM without local storage, the following minimum hardware specifications are recommended: Operating System: Linux Ubuntu 22.04 (LTS) Processor: 2 vCPUs Memory: 8 GB RAM Storage: 70 GB SSD Additionally, the following network requirements should be considered: Outbound Ports: 443 MQTT/AMQP over Websockets Furthermore, for the server to connect to the machine, it must be in the same subnet as the OPC UA server or be able to reach it.

Once you have identified a machine, system or process that you would like to monitor with the ai-omatic software, 7 steps are required. The aim is for your machine data to be continuously transferred to the ai-omatic Azure Cloud for analysis. Provision of the virtual machine. If possible, provision of a VPN for support by ai-omatic. Release of URLs Installation of the IoT Edge software environment. Establishing a connection to the OPC UA server of the pilot plant: Provision of OPC UA information. Establishing the network connection between the virtual machine and the data source (OPC UA) with any necessary port releases. We provide detailed instructions for carrying out these steps, which also take into account certain special features such as a proxy. With these steps, you are well equipped to start data acquisition / your project with ai-omatic.

Our predictive maintenance software is used in a wide range of industries. The manufacturing, energy, transportation and logistics industries in particular benefit from our solution, as failures of individual machine components often lead to expensive downtime of entire production lines. However, predictive maintenance is also used in the aviation, oil and gas and healthcare industries to minimize downtimes and ensure safety.

Reactive maintenance only reacts to problems after they occur, which can lead to unexpected failures and longer downtimes. Although preventive maintenance involves regular maintenance work, there is a risk of over-maintenance or unnecessary interventions, which can lead to high costs. In contrast, predictive maintenance uses advanced technologies to detect anomalies early and predict maintenance needs to minimize downtime and increase efficiency.

Input data such as sensor data, historical maintenance logs and operating data are used for the predictive maintenance of machines. This data is analyzed to detect patterns and identify potential problems at an early stage.

Predictive maintenance is crucial for companies in various industries today. This innovative maintenance strategy uses advanced technologies such as machine learning and big data analytics to continuously monitor the condition of assets and predict maintenance needs before failures occur. 1. Avoiding downtime and production losses: By detecting anomalies and potential problems at an early stage, companies can avoid downtime and production losses. This enables continuous and reliable production, which has a direct impact on the company's profitability and success. 2. Optimization of maintenance activities: By accurately predicting maintenance needs, companies can optimize their maintenance operations. Instead of relying on fixed schedules or carrying out reactive repairs, they can plan maintenance work in a targeted manner and deploy resources efficiently. The result is reduced maintenance costs and an extended system service life. 3. Increasing system availability: Predictive maintenance helps to increase the availability of systems by minimizing potential downtime. This is particularly important in industries such as manufacturing, power generation and logistics, where uninterrupted plant uptime is crucial. 4. Combating skills shortages: By accurately predicting maintenance needs based on data analytics, maintenance staff can plan more specifically and avoid unnecessary maintenance. This reduces maintenance effort and allows the team to focus on essential tasks, while less experienced staff can recognize anomalies early and respond appropriately. Overall, predictive maintenance helps to increase the efficiency and productivity of the maintenance team and ensure operational continuity. 5. Competitive advantages: Companies that rely on predictive maintenance have a clear competitive advantage in the market. They can adapt more quickly to changing market conditions, better meet customer requirements and be more agile and resilient overall. This lays the foundation for long-term success and future security.

The implementation of predictive maintenance measures requires the selection of a suitable software solution, the integration of sensors and the training of personnel to handle the new maintenance strategies.

Challenges include the integration of different data sources, the selection and, if necessary, retrofitting of suitable sensors, the need for qualified personnel for data analysis and the initial investment outlay. The aiomatic team of experts will support you every step of the way.

Beginnen Sie mit einer Analyse Ihrer Maschinen und Prozesse, um kritische Anlagen zu identifizieren. Wählen Sie dann eine geeignete Predictive Maintenance Software und integrieren Sie Sensordaten. Schulungen für Mitarbeiter und die schrittweise Umsetzung in Pilotprojekten sichern den Erfolg.

Predictive maintenance uses sensor data such as temperature, vibration and pressure to monitor the condition of machines in real time and detect anomalies at an early stage.

Predictive Maintenance Software reduziert Kosten, indem sie Ausfälle vorhersagt, ungeplante Stillstände vermeidet und die Lebensdauer von Maschinen durch gezielte Wartung verlängert. Außerdem werden Wartungszyklen optimiert und dieVerfügbarkeit von Maschinen erhöht, was zu einer besseren Produktionsleistung führt.