Hours delivered with passion
Problems solved with innovation
Project completed in timeline.
Different industries addressed
What is Predictive Maintenance?
It is based on more advanced mathematical methods that include statistical analysis, data mining, predictive modeling, machine learning, among others. Its function is to forecast events that will occur in the future thanks to the development of a prediction model.
Machine learning and artificial intelligence provide regular and predictive maintenance, repair and general processes used by companies to preserve their assets.
The critical role of machine learning is to build predictive accuracy.
- Prediction of failures and early alarms.
- Demand estimation and Prescriptive Maintenance.
- Prediction of results of processes according to the values of the variables (for example model of detection of anomalies in the quality of a product).
- Identification and Mitigation of the Human Factor.
- Intelligent Inventory and Supply Chain Management. • Computer Vision (Visual identification by computer of possible failures).
Predictive Maintenance Algorithm
The benefits of predictive maintenance
Predictive maintenance is a methodology, that is, a corporate philosophy that takes into account the condition of the equipment. Predictive maintenance periodically monitors machines based on the analysis of data collected through monitoring or field inspections using the full power and benefits of Artificial Intelligence and Machine Learning.
Reduction in the stock
of spare parts
Increase of the life
of the pieces
24% More parts time
Benefits of applying AI And ML in MRO
Machine failure reduction
Regular monitoring of the actual conditions of the machines and process systems can reduce the number of unexpected and catastrophic failures of the machine by an average of 55%. Projections indicate that with ML you can reach reductions of 90%.
Repair downtime reduction
Predictive maintenance reduces the real time needed to repair or recondition the factory equipment. The average repair time (MTTR) can be reduced by 60%.
Maintenance costs reduction
The actual costs normally associated with the maintenance operation can be reduced by more than 50%.
Reduction in the stock of spare parts
Costs that involve stock of spare parts can be reduced by more than 30%.
Increase of the life of the pieces
The prevention of catastrophic failures, and the early detection of machine and system problems increases the operational life of the industrial plant machinery by an average of 30%.
The availability of process systems increases aber the implementation of a predictive maintenance program based on condition. The increase can reach 30%.
24% More parts time
Unlike preventive maintenance the prediction helps to spend less on spare parts.
Satisfied customers who see us a partner in the task of saving and having a well maintained fleet.
Artificial Intelligence-driven companies are twice as likely to be market leaders. See how AI solutions help companies from various industries succeed.
Maximise yield efficiency with machine learning.
Diagnose smarter and improve well-being & quality of life.
Logistics & Transportation
Reduce maintenance costs, optimize routes and more.
Prevent fraud, manage risk and improve lending decisions.
Understand and reach your audiences more effectively.
Retail & E-Commerce
Convert ‘big data’ into valuable customer insights.
Get valuable insights from sensors, cameras, etc.
A different industry? Contact us to learn how we can help.
Some Of Our Case Studies
Predictive maintenance applications for MROClient: Aircraft manufacturer and maintenance company
Since 2019 (undergoing project) we are implementing a predictive maintenance applications for MRO (Maintenance, Repair and Overhaul) mostly for flight systems and turbofan jet engines predicting components failures and RUL (remaining useful life) for each piece using LSTM algorithms.
Crop yield predictionClient: Massive association of cereal producers
Development (ongoing project): from 2018 to the present, working for an Argentine farmers' association designing, training, evaluating and implementing models for: crop yield predictors (soybeans, corn, wheat and barley), zone-based management, pest and disease predictions for these four types of crops, nutrition deficit predictions and prediction of the moisture stress index of the crop canopy. I used optical satellite imagery and synthetic aperture radar imagery.
Classification model for OCT (Optical Coherence Tomography)Client: Local Society of Ophtalmologists
Development: in 2018 we developed a classification model for OCT (Optical Coherence Tomography) of five retinal diseases (Ophtalmology). The development involved to re- train and tune Inception v3 pre-trained models and a python user interface.