Independent Variable

Root Cause Analysis

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During the manufacturing phase, identifying the root cause(s) of an issue is a lengthy and painstaking process, performed with traditional methods, it’s also incredibly hard.
Today’s vehicles are highly complex, and each driver has unique behavior, maintenance actions and driving conditions. Some issues arise only under very unique circumstances that were unseen in the manufacturing process.

Machine learning techniques can vastly accelerate root cause analysis and speed resolution. Anomaly detection algorithms can analyze vast amounts of system and driver data efficiently. And they can perform this analysis using additional data types and in far greater quantities than traditional methods can handle.

For example, during the manufacturing phase, the use of image data as an input for root cause analysis helps organizations correlate failure modes to possible flaws in the underlying manufacturing procedures.

Benefits for the company

When an issue arises at any point in the product lifecycle organizations scramble to determine the exact cause and how to resolve it. The brand’s reputation (and possibly consumer safety) are at stake.

Feasability

High

Type of expertise/ AI domain

Image recognition and anomaly detection

Internal data required

Testing data, Sensor Measurements, Manufacturer Parameters, Tagged Images with issues, Data for Anomaly trends in production line

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