46%
Reduction in test time
83%
Increase in test throughput
$6M
Estimated savings over 3 years
LinePulse for Hydrogen Fuel Cell Manufacturing
Improve throughput in hydrogen fuel cell and electrolyzer manufacturing with process-specific machine learning in one easy-to-use platform.
Trusted by Leading Manufacturers Worldwide
Improve the hydrogen fuel cell production value chain
Acerta LinePulse is a flexible platform with a purpose-built toolkit, proven to make hydrogen fuel cell stack production more efficient.
1
Preventing scrap in plate manufacturing
An un-checked problem in plate production produces costly scrap. Acerta LinePulse analyzes manufacturing data and issues predictive quality alerts using statistical and machine learning methods. Problems are detected much earlier, reducing scrap rates.
2
Speeding problem resolution in stack assembly
Stacks are often assembled with components from other lines. LinePulse automates root cause analysis by uniting production data from multiple lines and locations. Problems are diagnosed in 7-12 minutes, avoiding resource-intensive investigations.
3
Accelerating stack assembly FAT
Reducing stack assembly test cycle time is critical to scale production. Acerta’s ML model predicts test outcomes early. Read how test times were reduced by 46% for Ballard, improving throughput by 83% without sacrificing quality.
4
Improving first time through in engine assembly
Fuel cell scrap is costly and rework risks warranty claims, meaning high first-time-through rates are essential. Acerta LinePulse diagnoses the cause of problems quickly to minimize scrap, and sends alerts when models detect early indicators of defects.
This increase in efficiency allows us to scale production
“Acerta’s machine learning model allowed us to cut our fuel cell stack testing time by 46% without sacrificing quality. This increase in efficiency allows us to scale production while lowering our hydrogen and labour costs, and avoiding additional capital investments.”
Andreas Putz
Principal Engineer: Data Science
Principal Engineer: Data Science
This increase in efficiency allows us to scale production
“Acerta’s machine learning model allowed us to cut our fuel cell stack testing time by 46% without sacrificing quality. This increase in efficiency allows us to scale production while lowering our hydrogen and labour costs, and avoiding additional capital investments.”
Andreas Putz
Principal Engineer: Data Science
Case Study
Reducing fuel cell
stack factory
acceptance
testing by 46%
Objectives
Accelerate fuel cell production by reducing test times and avoiding costly test station expansion using machine learning.
Challenge
Lengthy test cycles, underused data, and high capital costs made it difficult for Ballard to scale production and meet growing demand efficiently.
Key Results
- 46% reduction in average test time per unit
- 83% increase in testing throughput
- Over $6M projected savings in capital and operating costs
Case Study
Reducing fuel cell stack factory acceptance testing by 46%
Objectives
Accelerate fuel cell production by reducing test times and avoiding costly test station expansion using machine learning.
Challenge
Lengthy test cycles, underused data, and high capital costs made it difficult for Ballard to scale production and meet growing demand efficiently.
Key Results
- 46% reduction in average test time per unit
- 83% increase in testing throughput
- Over $6M projected savings in capital and operating costs
Predictive algorithms
with a problem-solving toolkit
Deploy predictive models and report instantly with flexible statistical tools all in one platform.
