A leading US based engineering service provider which is handling assets across 200+ airports Every delay in flight or asset down time Invites a penalty resulting in 10-50 million dollars of leakage per year. The company wanted to create a new revenue streams by offering asset prognostics as a service.How Cerebra Solved it ?
Cerebra IOT Prognostics/Diagnostics platform was connected to non-powered and powered assets like cargo container, tow bars, baggage containers, cargo loaders, De-icers, Push back trucks Cerebra picked up hidden signals buried deep within billions of machine events like coolant temperature changes, unscheduled maintenance events, pressure alarms, hard accelerations, geo fencing violation events and recommended surgical operational actions in real time across 35 asset types.The impact on outcome
A new value creating business offering generating new revenue stream for the customer and saving millions of dollars for the airports. This is being scaled to 200+ airports across 30+ countries.
One of the World’s largest manufacturers of Shale field equipment and natural gas/diesel engines based in Houston is heavily dependent on OEMs to minimize asset failures and downtime: There were 2 specific problems they wanted to solve.
Create new business models for Prognostics and diagnostics as a service How to proactively auto generate spare part requests triggered by sensor events and thereby reduce inefficienciesHow Cerebra Solved it
Cerebra IOT Prognostics/Diagnostics platform ingested signals from a variety of frontline industrial assets in real time like - Acidizing units, Fracking pumps, chemical additive units, blenders, large generators,. The specific signals analyzed include Pressure signals , Oil temperature signals, horse power signals, rpm signals, discharge pressure signals to: find Anomalies to Potential Fault Modes, predict Failures & Maintenance Requirement and Forecast Spares requirement.Impact on outcome
Machine data becomes a new source of revenue using Cerebra IOT Prognostics/Diagnostics platform with a revenue generation of 120 million+ across first 3 years.
Safety is one of the most complex subjects in the Oil & Gas industry. Traditional approaches to safety such as Process Safety and Occupational safety tend break down the subject into multiple silos and track lagging indicators of safety rather and leading indicators. As such, last mile real-time visibility into safety risk exposure is extremely limited, and one of the largest hydrocarbon producers in the world was facing this very problem.How Cerebra Solved it
Safety Incidents happen as a result of man-machine interactions and the processes that govern these interactions. Cerebra ingested millions of machine signals from offshore assets, along with process, and people related signals and quantified safety risk exposure related to Assets, Processes, and People through sophisticated risk modeling algorithms. Signals ingested include machine events such as leakages, pressure changes and flow count, process related data such as audits and maintenance, and people related data such as operator expertise.Impact on outcome
The offshore operations team has visibility into safety risk exposure across their rigs, and were able to ensure that they had the right information at hand that would be necessary to take proactive action to avert potential safety incidents due to machine failures.
The customer is one of the Largest adhesive manufacturer in world with 133 plants globally Adhesives used in aviation, automotive and electronic industries. The problem they faced was to increase the quality and yield of the factory lines by controlling 2 levers - viscosity and softening points.How Cerebra Solved it
Cerebra IOT signal intelligence platform was ingested 3 years of sensor data regarding plant operations from temperature sensors, rpm sensors, torque sensors and pressure sensors which were strapped on to industrial mixers Cerebras ensemble models used to filter signal from noise and specifically identify the contributors to quality. This process was Scaled to 33 plants, 1400 manufacturing lines and 16 event types cumulatively streamed in 20 million sensor events analysed.Impact on outcome
More than 140 million dollars of savings from defective products across 3 years.
One of the key problems hydroelectric plants face is that of limited PLF. Moreover, they have generation commitments with Regulatory bodies that they have to uphold to help manage peak loads, as well as fulfil general overall power demand.How Cerebra Solved it
Cerebra IOT signal intelligence platform ingested electro mechanical signals from Small Hydroelectric plants, and detected signals to:
Predict Failure events Reduce Downtime and Maximize Uptime Predict Maintenance Requirement Forecast Spares requirement Optimize GenerationImpact on outcome
Overall the Hydro plants were able to Increase PLF , Optimized generation capacity and Minimize Regulatory Penalties.
The largest North American Vessel Classification Society, that has classified more than 13,000 vessels is embarking on a new journey to help the vessels capture instantaneous parameters. The company then wanted to understand the parameters affecting fuel consumption, and hence predict the fuel consumption as a function of these parameters thus leading to an improved performance of the vessel.How Cerebra Solved it
Cerebra IOT Diagnostics/Prognostics platform was connected to monitor the 70+ instantaneous parameters streaming in from various vessels every 10 seconds. Cerebra picked up those predictors that had a significant influence on the fuel consumption, so as to build a predictive model to predict the fuel consumption under given set of conditions. The model learns and improves by itself based on the new data that gets streamed. The model was also able to pick up data anomalies that led to the conclusion that certain sensors on the vessels weren’t performing as per their specifications.
With the large amount of vessel performance data collected from various vessels across different vessel categories, the Cerebra platform was further able to provide benchmarks w.r.t. Fuel consumption for similar class of vessels. These benchmarks could further be utilized while designing & building new vessels, or to identify if any retrofits could be added to improve the performance of existing vessels based on benchmarking results.The impact on outcome
Optimizing vessel speed to minimize Total Fuel Consumption (Propulsion Fuel Consumption + Electrical Generation Consumption) and thus improve the performance of the vessel resulted in savings upto $300,000/year for large vessels.
Global Fortune 100 company innovating with off grid solar solutions.The biz problem
Offer a new revenue stream where solar energy as a service.Solution highlights
Cerebra was connected Photovoltaic cells, Inverters, Batteries, GRID, DC Loads.Scale
100+ sites spread across two continents 100,000 events per site per yearImpact on outcome
Projected saving of a million dollars over 5 years by replacement of diesel generators and accurate sizing of solar panels.