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
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Aquire & Preprocess Data
We will collect a large set of sensor data representing normal and faulty operations to process them.
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Identify Conditions Indicators
Indicators are used to distinguish and understand normal and faulty condition.
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Train Model
We will detect anomalies. We will train a classifier to detect different types of failures, predict degradation, and estimate times.
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Deploy & Integrate
Once the algorithm has been developed, it will be put into operation by deploying it on the Cloud or on your conventional device.
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.
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Maintenance costs
reduction -
Machine failure
reduction -
Repair downtime
reduction -
Reduction in the stock
of spare parts -
Increase of the life
of the pieces -
Production increase
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24% More parts time
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50% improvement
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%.
Production increase
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.
50% improvement
Satisfied customers who see us a partner in the task of saving and having a well maintained fleet.
We Transform
Companies
Artificial Intelligence-driven companies are twice as likely to be market leaders. See how AI solutions help companies from various industries succeed.
Manufacturing
Maximise yield efficiency with machine learning.
Healthcare
Diagnose smarter and improve well-being & quality of life.
Logistics & Transportation
Reduce maintenance costs, optimize routes and more.
Finance
Prevent fraud, manage risk and improve lending decisions.
Marketing
Understand and reach your audiences more effectively.
Retail & E-Commerce
Convert ‘big data’ into valuable customer insights.
IoT
Get valuable insights from sensors, cameras, etc.
Your Industry
A different industry? Contact us to learn how we can help.
Some Of Our Case Studies
Predictive maintenance applications for MRO
Client: Aircraft manufacturer and maintenance companySince 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.
Tools used:
Crop yield prediction
Client: Massive association of cereal producersDevelopment (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.
Tools used:
Classification model for OCT (Optical Coherence Tomography)
Client: Local Society of OphtalmologistsDevelopment: 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.
Tools used:
Awards & Accreditations
