Findability Sciences offers an AI-powered predictive maintenance platform that integrates various data types to forecast equipment failures across multiple industries, including aviation. Their self-learning algorithms enable accurate predictions of component failures, significantly reducing maintenance costs and improving fleet reliability. The solution also features automatic data processing and model generation, allowing for continuous improvement and adaptability to changing data conditions.

1. AI-POWERED PREDICTIVE MAINTENANCE
© 2018 Findability Sciences

2. ❑ Unifies Structured, Unstructured,
Internal and External Data.
❑ Text Analytics, Natural Language
Processing, Cognitive Analytics.
❑ Artificial Intelligence (AI) provided
through self-learning, fully automatic
multi-modelling algorithm.
❑ Cloud Based. SaaS or DaaS/PRaSS
FINDABILITY PLATFORM®
❑ Financial Services
❑ Manufacturing
❑ Pharma/Health
❑ Retail
❑ General Trading
❑ Higher Education
❑ Automotive/Transportation
INDUSTRIES SERVED
❑ Digital Body Language (DBL)
❑ Customer Persona
❑ People Analytics
❑ Fraud Detections
❑ Price Predictions
❑ Inventory Predictions
❑ Predictive Maintenance
❑ ChatBot
SOLUTIONS
FINDABILITY SCIENCES ➢ Developer of award winning Findability Platform®, a leading
Bigdata, Cognitive and AI Platform.
➢ SoftBank Corp’s Portfolio Company.
➢ Joint Venture with SoftBank in Japan.
➢ Erstwhile Member of Partner Advisory Board of IBM Watson™.
➢ Investor in Boston based Soft10 Inc.
➢ Global Presence: Boston, Tokyo, Mumbai and Aurangabad.
ECOSYSTEM PARTNERS
© 2018 Findability Sciences

3. FINDABILITY SCIENCES KK
• Strong presence in Japan
• ~1,000 B2B sales staff
• Telecom, AI, Robotics
• Watson Partnership
• Largest vision fund
• Tech IP
• Service delivery
• Industry agnostic solution
• Watson partnership and
experiences
AUTOMATED PREDICTION, AI CHAT-BOT AND NLP Q&A, COGNITIVE & BIG DATA
ANALYTICS © 2018 Findability Sciences. Confidential

4. AI-POWERED PREDICTIVE MAINTENANCE FOR AIRLINE
FINDABILITY PLATFORM®

5. CUSTOMER
International airline in United States
PROBLEM
Wanted to predict defects specific to Air Conditioner ( ATA
21), Pressurization (ATA 36), and flight control (ATA 27)
failures in fleets.
SOLUTION
Findability Platform® self learning AI machine to identify
component failure in advance.
• Data:Defects and delays data related to A320 and E190
fleet specific to ATA 21, ATA 36 and ATA 27
• Training: Automatically learns from historical data
• Prediction: Predict for next possible component failure
BENEFITS
• 88% accurate prediction
• Minimize corrective and breakdown maintenance
• Help to maintain satisfactory equipment conditions, and
improves fleet reliability
• Minimizes the risk of aircraft failure
• Saves significant money
© 2018 Findability Sciences
IDENTIFY COMPONENT FAILURE

6. CUSTOMER
International airline in western Pacific region
PROBLEM
Wanted to know flight engine failure in advance to convert
unscheduled maintenance to planned maintenance and
remove chaos
SOLUTION
Findability Platform® self learning AI machine to predict
failure from 60-90 days in advance.
• Data: Flight engine log, flight information system log,
pollution data, flight path data
• Training: Automatically learns from failure engines (left,
right, international, domestic).
• Prediction: Predict for next possible engines failure.
BENEFITS
• 90% accurate prediction.
• 97% accuracy in top 3 decile. Customer uses top 3 decile
result to schedule immediate engine inspection.
© 2018 Findability Sciences
ENGINE FAILURE PREDICTION

7. PREDICTION APPROACH - FINDABILITY SCIENCES
HISTORICAL INFORMATION
insight
SELF- LEARNING
Predict
PREDICTIVE ANALYSIS
© 2018 Findability Sciences
Cleaning, Combining & enrichment of
data
With out human intervention the
solution develops predictive models
Continuous improvement and
automation of the result
With the use of API connection the
solution automatically runs and
complies heterogeneous data
Data is processed through a self-learning
algorithm to develop models depending
on use case
The solution automatically calculates
relevant score and ranks models in order
of probability

8. PREDICTIVE MAINTENANCE APPROACH - FINDABILITY SCIENCES
FAILURE HISTORY - The failure history of a machine or
component within the machine.
MACHINE CONDITION - The operating characteristics
of a machine, e.g. data collected from sensors.
REPAIR HISTORY - The repair history of a machine, e.g.
previous maintenance records, components replaced,
maintenance activities performed.
MACHINE FEATURES - The features of machine or
components, e.g. production date, technical
specifications
OPERATING CONDITIONS - Environmental features
that may influence a machine’s performance, e.g.
location, temperature, other interactions.
SELF-LEARNING - Learns from historical data to
generate continuously updated results that accommodate
ever-changing data
AUTOMATIC - Unsupervised, fully autonomous multi-
step modelling and prediction process that produces
results faster
NON PARAMETRIC - Based on Conditional Probabilities
no assumptions to limit the best models.
MULTI MODELING - Generates and selects multiple
models from a data pattern, making use of vast volumes
of unstructured data in addition to structured data
Detect anomalies in equipment or system performance or
functionality.
Predict whether an asset may fail in the near future.
Estimate the remaining useful life of an asset.
Identify the main causes of failure of an asset.
Identify what maintenance actions need to be done, by
when, on an asset
Collect & Clean Data
HISTORICAL INFORMATION
insight
Identify Pattern
MACHINE LEARNING
Make Prediction
Predict
PREDICTIVE ANALYSIS
© 2018 Findability Sciences

9. © 2018 Findability Sciences
PREDICTION PLATFORM METHODOLOGY
SELF LEARNING, AUTO MULTI-MODELING,
FULLY AUTOMATIC PREDICTION
HISTORICAL AIRCRAFT
INFORMATION
ONGOING
DAILY/WEEKLY/ MONTHLY DATA
1
SELF-LEARNING PREDICTION
M1
M2
M3
M4
M..
Findability Platform®
AUTO MULTI-MODELLING
2
3 4
5
DAILY/ WEEKLY/
MONTHLY
PREDICTIONS WITH
ASSOCIATED
PROBABILITY

10. • Reduce operational risk of mission critical
equipment.
• Increase rate of return on assets by
predicting failures before they occur.
• Control cost of maintenance by enabling
just-in-time maintenance operations.
• Lower inventory costs by reducing inventory
levels by predicting the reorder point.
• Discover patterns connected to various
maintenance problems.
• Estimate remaining lifespan of assets.
• Recommend timely maintenance activities.
BENEFIT
© 2018 Findability Sciences

11. Build future with AI
Vivek Vij
vivek@findabilitysciences.com
Direct: +1 781-353-2466
Findability Sciences 300 TravelCenters Drive STE 4690 Woburn MA 01801