Internship - AI Engineer – Development of Fault Diagnostics and Prognostics solutions for Complex Systems (6 months)

Key Skills

Aartificial intelligence, Algorithms, Augmented Reality (AR), dense 3D reconstruction, Feature Extraction, Keras software library, Large scale deployment, Mathematical optimization, Mathematics, Natural Language Processing (NLP), Python Programming, PyTorch, Regression Analysis, Statistical modeling, Statistics, TensorFlow, Terabyte scale datasets

Job Description

About Us:

We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

Our Purpose

Together, we create amazing technology that unlocks access to energy for the benefit of all.

You can find out more about us on https://www.slb.com/who-we-are

Location: Clamart, France

Come and Join SLB’s AI Lab in Paris. We are currently offering internship to bright minds specialized in Data Science and Artificial Intelligence. Discover a multinational company. We have brought a little bit of the Silicon Valley in Paris. Experience working within a team of young and fun passionate Data Scientists, tackling real business challenges, in tandem with business experts who are sitting at your desk.

The Artificial Intelligence & Machine Learning Data Scientist helps develop software and processes that can be used for robotics, artificial intelligence programs and application. In close collaboration with the business and métiers, the data scientist offers mathematical and statistical models from the collected data to augment, improve or speed up human decisions within the Energy sector. With SLB you will be given the opportunity to apply your expertise and deploy deep learning solutions at scale on real-world problems, supporting many areas of SLB’s business. SLB is the first Energy service company to move its processes and workflows in the cloud. This gives the Embedded AI Lab the perfect opportunities to leverage these innovative technologies and resources. As part of the Embedded AI Lab you will be able to test, experiment and research with the bleeding edge environment with Petabits and Petabits of data. You will be in charge of applying research and delivery of Proof of Concepts solutions, responding to a clear and specific business needs.

Context

Predictive Maintenance (PdM) team in SRPC-SLB AI Lab working on development of smart condition monitoring solutions for the clients to improve their operational efficiency, reduce costs and maximize productivity by turning operational data into actionable insights. The Predictive Maintenance techniques can predict asset failure times, detect anomalies (i.e. incipient faults) by accessing multiple data sources in real time so that the clients/organizations can avoid costly system downtime and improve reliability, availability and safety in their organization. The early identification/detection of asset failures can help maintenance technicians to minimize system downtime, deploy limited maintenance resources more cost-effectively and enhance flexibility and efficiency of their systems.

AI Lab PdM team aiming to offer a real-time smart condition monitoring services so called Digital Twins for its clients to improve reliability, availability and safety of their equipment via smart predictive maintenance solutions. Hence, in this context, efficient diagnostics and prognostics techniques will be studied under Digital Twins program for complex systems (i.e., such as pumps, motors, safety valves, artificial lift components, CNC machines, etc.) condition monitoring.

Description:

Different mechanical faults in Complex systems (pumps, motors, safety valves, artificial lift components, CNC machines, etc.) will be the main course of this research by using different analytical methods. At first critical component analysis should be performed in order to select the most critical component next to the failure mode analysis (FMA). Then, the adequate sensors and data acquisition systems should be studied on the basis of a selected component. For the test(s)/data collection a test protocol has to be written in advance. Different sensory data such as temperature, flow rate, acceleration and pressure will be collected next to other alternative information (i.e., asset condition/incident reports).

The obtained signals/information will be processed to extract relative component health indicators for fault classification and failure time prediction. The result of this study should be summarized by the intern as a report towards an application of predictive maintenance in a proof of concept Digital Twins for Complex systems.

You will learn and practice:

• Time-series and sequential data processing, asset health management
• Testbench installation, data acquisition systems
• Real time signal processing, leading deep neural network structures
• Textual data processing, dashboards and KPI monitoring

Required skills:

• Strong research and analytical skills
• Knowledge in fault diagnostics, prognostics and condition monitoring is a plus
• Programming skills: Python
• Knowledge in Machine Learning, Deep Learning and Statistical Analysis, NLP
• Fluency in English

SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.