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Predictive Maintenance

Predictive maintenance for manufacturing and process industries - Why is it important?

Industries have been working around the health of their machinery as a reaction to malfunction. Predictive maintenance as a principle suggests that companies mine data about the machinery and formulate data models that can help them in maintenance use cases.
AI Predictive maintenance

Why predictive maintenance is key?

Optimize resources and reduces mean time to repair (MTR)

Reduced maintenance costs by performing repairs when actually needed

Reduce unplanned maintenance downtimes

Avoid unplanned maintenance: Minimize unplanned downtime and catastrophic failures that put your business at risk.

Root-cause analyses: Find causes of equipment malfunctions and work with supplies to switch off reasons for high failure rates, increasing return on assets.

Why Predactica is the best choice for your predictive maintenance problems?

Within the confines of a project, every stakeholder will be able to manage their application’s assets under one roof and collaborate with other stakeholders as well.

Predactica’s ML platform and compute environment are centralized, scalable, and cost-effective

Non-technical business users will be able to test business hypotheses and build production-ready ML solutions just as well as data engineers and data scientists would. Our solutions are for everyone!

Predactica’s AutoML model allows users to train multiple models simultaneously and evaluate them side by side. The users can pick the best models after careful evaluation.

Predactica’s platform does not stop with just coming up with data models. It goes on to explain and interpret Black Box models.

The MLOPS feature detects model drift and bias in model recommendations.

Predactica ML platform leverages advanced Deep learning Models for better accuracy and makes intelligent recommendations that use case-dependent and data-driven

How Can Predactica Help The HVAC Industry?

The Heating, Ventilation, and Air Conditioning industry thrives on the success of its machinery. Predactica helps stakeholders predict possible equipment failures based on historical data. IoT sensors inside these HVAC systems collect and transmit data that will then be used for predictive maintenance. Here are a few benefits –
The HVAC Industry

Anomaly detection for fraud detection, cyber security, and predictive maintenance

Anomaly detection is one of the common machine learning tasks that looks for outliers in the way data points are normally distributed. While it doesn’t necessarily have to be time series data, anomaly detection often goes hand in hand with it. Detecting anomalies in time series entails finding irregular spikes or valleys that significantly deviate from the way seasons and trends look.
AI Demand Forecasting Use Cases

Got Snowflake? Then you need Snowdactica!

Use predactica’s tool that’ll make use of historic data to deliver actionable insights.