Predictive Maintenance
Predictive Maintenance uses data for intelligent monitoring of machine behavior to reduce premature intervention costs and avoid catastrophic failures. Service intervals can be optimized by inferring equipment health-state information from sensor data. The result is smarter operations – higher uptime and lower overall costs. MATLAB is the ideal tool for implementing the predictive maintenance workflow.
Data Preprocessing
MathWorks Consultants provide assistance with the application of data consolidation, cleaning, signal processing techniques, to handle distributed data, missing and invalid data, as well as outliers and noise. The result is a structured dataset amenable to analysis and model development.
Exploratory Analysis
We help you to efficiently and systematically investigate your data, including cases where the relationship between sensor measurements and outputs, e.g. time to failure, is not well-understood. Visualization and data analysis tools like curve-fitting, system identification, and signal analyzer apps can be used to test hypotheses and gain rapid insights. Dimensionality reduction, feature ranking and selection methods can be applied to prepare for model development.
Predictive Modeling
If your data points are not labeled, we help you apply unsupervised machine learning approaches to detect patterns and anomalies in your measurements. We show you how to visualize and analyze changes in measurements due to age, i.e. infer equipment aging trajectories in feature space. We help you to identify and visualize clusters occurring in your data and assist with labeling these categories.
If data points are labeled, we help you create and compare a wide range of classification and/or regression models to identify the root cause of failures and estimate remaining useful life respectively. We can help validate and refine the highest-performing model and investigate feature transformations to increase accuracy. Features selected inform decisions about which sensors provide the most meaningful information.
Operational Deployment
Once a predictive model has been developed, we help you put it into production. Thresholds and performance metrics are selected to finalize your control procedures. We then work with you to deploy the C / C++ and/or HDL code, automatically generated from your algorithms, to a ‘smart’ device, a microcontroller or a phone. We also help you implement IoT analytics, on the cloud or on-premise.
MathWorks Consultants help you to:
- Determine appropriate data preprocessing, feature selection and predictive modeling techniques, and apply these to your data
- Transfer knowledge and best practices to you to build in-house competency through customized, project-based coaching sessions
- Release your revised procedures into production, to reduce your maintenance and operating costs
“MathWorks Consulting’s support is among the best I’ve seen; the consultants are fast and exceptionally knowledgeable. We’ve already seen a positive return on investment from cost savings, and now we have more budget and time to complete more machine learning projects that will provide similar benefits.”
— Herr Dr. Michael Kohlert, Mondi Gronau GmbH
