Maintenance Engineer-Intelligent Maintenance_PS_RBCD

Job Summary

• Takes responsibility for maintenance of defined MAE
• Focused on the maintenance and repair of machines related to fuel cell business
• Ensuring the efficient operation of equipment, promptly addressing technical issues, and participating in equipment optimization and improvement.
• In addition, actively explores and drives the transition from traditional to intelligent maintenance by leveraging PLC software, equipment interconnectivity, and digital tools to achieve predictive and proactive maintenance.

Roles and Responsibilities

1. Main maintenance KPI of responsible machines

• Malfunction regular review
• Leading problem solving of key malfunction, utilizing PLC software for deep-level fault diagnosis and program optimization.
• KPI PDCA L3/L4 preparation
• Activities for machine performance stabilization and continuous improvements, including data-driven analysis and digitalization initiatives.

1. Spare parts preparation

• Defining spare parts
• Tracking spare parts abnormal usage.
• Working at alternative solution if spare parts not available.
• Support spare parts localization project
• Organizing repaired key spare parts try-out

1. Maintenance competence development for responsible machines

• Record and analyze equipment failure data, providing improvement recommendations. (Explore data pattern for predictive maintenance)
• Following the competence development plan
• Attending know-how exchanging activities in IPN
• Train and guide other team members to enhance equipment maintenance skills. (Promote digital and intelligent maintenance awareness within the team)

1. Collaborate with suppliers and internal teams to ensure timely repairs and technical support.
2. Performing at TPM activity from maintenance

• Regular review and update of PM items
• Key malfunction (long breakdown or repeated problem) summary (PSS) and defining standard reaction plan for same problem (error search plan), preferably digitizing these plans into system-based decision trees.
• Joining TPM activities, taking a main role in TPM review meeting, supporting to improve TPM maturity

1. Supporting planning and releasing of new investment machines

• Supporting technical specification defines for the new investment machine. (Ensure new machines meet interconnectivity and data acquisition standards for intelligent maintenance)
• Reviewing and summarizing historical machine problem and machine concept failure and related improvements; Evaluating the MAE potential improvements
• MAE releasing and MAE A/B/C classification according the standard check list.
• Supporting MAE commissioning

1. Technical preparation for machine overhauling

• Preparing the evaluation check list for machine overhauling
• Preparing the overhauling proposal
• Coordinating the overhauling project

1. Contributing to cross-functional improvements, Eg.CO2 reduction, System CIP projects, Shop floor CIP projects, etc.
2. [Preferred] Explore and pilot emerging technologies (e.g., AI algorithms, machine learning) in equipment condition monitoring and predictive maintenance to adapt to rapid technological iterations.

• Education degree:  Bachelor or Master degree in Mechanical Engineering, Electrical Engineering, Computer Science or a related field.
• Total working experience  > 5 year
• Maintenance related working experience >3  years
• Strong fault analysis and resolution skills. Solid foundation and hands-on experience in PLC software for troubleshooting and logic optimization.
• Good teamwork and communication abilities.
• Fluent English both in speaking and writing
• Interest and foundational knowledge in equipment interconnectivity (IoT), data acquisition, and digital/intelligent maintenance systems.
• Highly curious, open to trying new things, and embraces challenges to adapt to rapid technological changes.
• (Preferred) Basic knowledge or strong passion for Artificial Intelligence (AI), machine learning, or data analytics, with a willingness to apply them in industrial maintenance scenarios. Candidates with relevant hands-on experience or self-driven AI learners will be given priority.

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