Engineers use MATLAB, Simulink, and Predictive Maintenance Toolbox to develop and deploy condition monitoring and predictive maintenance software to enterprise IT and OT systems.
Access streaming and archived data using built-in interfaces to cloud storage, relational and nonrelational databases, and protocols such as REST, MQTT, and OPC UA.
Preprocess data and extract features to monitor equipment health using apps for signal processing and statistical techniques.
Develop machine learning models to isolate root cause of failures and predict time-to-failure and remaining useful life (RUL).
Deploy algorithms and models to your choice of in-operation systems such as embedded systems, edge devices, and the cloud by automatically generating C/C++, Python, HDL, PLC, GPU , .NET, or Java based software components.
Access Data Wherever It Lives
Data from equipment can be structured or unstructured, and reside in multiple sources such as local files, the cloud (e.g., AWS, S3, Azure, Blob), databases, and data historians. Wherever your data is, you can get to it with MATLAB. When you don’t have enough failure data, you can generate it from a Simulink model of your machine equipment by injecting signal faults, and modeling system failure dynamics.
Clean and Explore Your Data to Simplify It
Data is messy. With MATLAB, you can preprocess it, reduce its dimensionality, and engineer features.
Remove noise, filter data, and analyze transient or changing signals using advanced signal processing techniques.
Simplify datasets and reduce overfitting of predictive models using statistical and dynamic methods for feature extraction and selection.
Detect and Predict Faults Using Machine Learning
Identify root cause of failures and predict time-to-failure using classification, regression, and time-series modeling techniques.
Interactively explore and select the most important variables for estimating RUL or classifying failure modes.
Train, compare and validate multiple predictive models with built-in functions.
Calculate and visualize confidence intervals to quantify uncertainty in predictions.