Combining sensor data with advanced analytics and artificial intelligence improves management of engines, planes, vehicles, machines, and other high-value assets. Predictive maintenance uses real-time data to monitor system performance, to identify faulty sensors, and to detect, predict, and avert outages. It cuts costs, boosts performance, reduces downtime, and prevents failures. While the roots of predictive maintenance lie with work to improve availability of British military aircraft in World War II, the present lies with distributed sensors, the industrial internet of things (IIoT), big data analytics, and AI/ML (artificial intelligence/machine learning).
It’s a scene familiar to every driver– you get behind the wheel and the check engine light pops on. Is it serious or ignorable? And, per a popular meme, when the light pops off, has the engine magically fixed itself or is the check engine light also broken? This light is triggered by errors from multiple sensors and can mean anything from a loose gas cap to a seriously misfiring engine. One of the most common causes is actually a bad connection between the controller and a sensor; that is, the fault is often with an engine sensor and not the engine at all!
Assets such as advanced aircraft and weapons platforms have thousands of diverse and interconnected sensors. On the one hand, this yields massive data sets containing very valuable insights on system performance and failure mechanisms. On the other hand, sensors can and will malfunction, due to defects or compromises, creating anomalies in the data that hamper effective use.
With a global market projected to reach $64.25 billion by 2030, predictive maintenance offers substantial benefits across the product lifecycle, from development, test, and evaluation to operation, maintenance, and upgrade, and to diverse sectors – industrials, manufacturing, defense, aerospace, healthcare, utilities, and energy. Harnessing sensor data to achieve these benefits requires state-of-the-art statistical techniques, efficient ML algorithms, and high-performance analytics systems. Peraton Labs leverages a breadth of technical and domain expertise to develop predictive maintenance solutions for challenging applications.
- Data fusion: Techniques for efficient processing and fusion of very large sets of data, including data scrubbing, standardization, anomaly detection, error-correction, and reconciliation. We have decades of experience developing robust ETL (extract, transform, load) procedures for large sets of data and metadata and processing large data volumes (terabytes), high data diversity (e.g., fusing historical and current data), and uneven data quality.
- Times series analysis: Sophisticated methods for modeling and analyzing multi-variate time series. Sensor data, whether regular readings over units of time or work or alerts on conditions, consists essentially of a series of data points over time. Our deep expertise in statistical analytics for multivariate time series is critical for modeling data from multiple sensors and supporting sound inferences.
- Efficient AI/ML algorithms: Cutting-edge machine learning algorithms for automated and highly accurate analyses. We leverage state-of-the-art AI/ML to develop models and forecasts that are tailored and adapted for specific use cases, such as cause-and-effect analysis, pattern recognition, and prognostics. For example, applying our unsupervised ML algorithms to sensor data from multiple airplane platforms, we can successfully detect anomalies in the fused sensor data and identify the specific sensors that are mis-operating due to error or compromise.
- Real-time analytics systems: User-friendly systems, supporting data exploration, trend analysis, user queries, and visualization. We have developed and deployed analytics systems across diverse domains -- aircraft testing, smart grid operations, financial transactions, and clinical trials, to name just a few. Our systems feature easy-to-use dashboards, advanced query capability, and high-value visualizations.
Contact us at email@example.com to learn more about our capabilities in predictive maintenance and how we can help you:
- Avoid downtime and increase reliability
- Extend the life of assets and decrease capital expenditure
- Reduce unnecessary maintenance
- Lower cost and complexity of repairs
- Improve management of parts, materials, and inventory
- Boost performance and productivity
- Meet regulatory requirements and compliance