The O&M optimization and operation management system helps to quickly pinpoint problems and recommends the right maintenance solutions for fixing the problems.
Using our solution, a carmaker in the Fortune Top 500 list achieves faster troubleshooting in after-sales maintenance services, eliminating the heavy reliance on manual work in quality risk monitoring, maintenance suggestions, and problem feedback. With the volume of maintenance case data growing over time, maintenance case classification and problem identification are improved in terms of both quality and efficiency.
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Introduction

A car maker
某世界500强车企,将售后质量风险监控、维修建议反馈问题摆脱以人工为主推动。
Project Results
  • 1. Solidified expert experience for knowledge accumulation

    NLP (natural language processing) is performed on a large number of maintenance orders to acquire relevant knowledge, which is connected through knowledge graphs to build a knowledge graph database. In this way, expert experiences are solidified into long-lasting knowledge. 

    2. Fault inference

    In case of a fault, inferences are drawn from the data of the faulty node, vehicle model, and parts to help identify the root cause.

    3. Semantic retrieval

    The system performs semantic analysis based on the fault description to quickly find the similar cases and recommend applicable maintenance solutions.

Related Product
O&M Optimization and Operation Management
Allows equipment fault diagnosis and health management for a better O&M decision-making
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