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Arc Exclusive
Arc Exclusive

Machinery Diagnostics Engineer - Part-Time - Overlap with Central Timezone (US)

Location

Remote restrictions apply
See all remote locations

Hourly rate

Hourly rate

Min. experience

3 - 5 years

Hours per week

20 hours

Duration

24 weeks

Required skills

DatabricksPython

Freelance job
Posted 24 days ago
Apply now
Actively recruiting / 31 applicants

We’re here to help you

Cynthia is in direct contact with the company and can answer any questions you may have. Email

CynthiaCynthia, Recruiter

Project Overview

We’re assembling a specialized development team to build a next-generation data analytics platform designed for customers in industrial markets with critical assets. This product will enhance asset reliability and performance through advanced diagnostics and AI-powered insights.


Key Responsibilities

  • Product Development: Combine expertise in machinery dynamics with strong Python skills to contribute to the development of an industrial data analytics platform focused on critical machinery.
  • Diagnostic Analysis: Apply knowledge of vibration analysis, kinetics, rotor dynamics, electrical signature analysis, and time-domain waveform analysis to detect and resolve machinery issues. Familiarity with Finite Element Analysis (FEA) is a plus but not the main focus.
  • Diagnostics Translation: Transform vibration, rotor dynamics, and electrical analysis techniques into Python-based algorithms and models.
  • Model Development: Build and validate machinery models using signal processing and diagnostic techniques for real-time monitoring.
  • Data Structuring: Organize diagnostic outputs into structured, scalable formats compatible with cloud-based repositories (e.g., Databricks).
  • Technical Documentation: Produce clear documentation for diagnostic algorithms, modeling assumptions, and code implementations.
  • Procedure Development: Develop standard procedures for collecting and analyzing vibration, electrical, and kinetic data, and share these with internal teams.
  • Collaboration: Work with partners and vendors to define diagnostic specifications, troubleshoot issues, and design new diagnostic methodologies.
  • Tool Enhancement: Recommend improvements to diagnostic tools and reporting formats based on real-world and modeled data.
  • Methodology Improvement: Continuously refine diagnostic methodologies, tools, and output formats, including vibration reports, electrical analysis, and FEA models.
  • Software Development Support: Collaborate closely with the Software and R&D teams to support the creation of a strategic software platform offering reliable diagnostic tools for industrial asset management.

Deliverables

  • Implement unsupervised learning techniques to identify critical features in emerging failure modes.
  • Support the Data Science team in building and tuning machine learning models for machinery health diagnostics.
  • Deliver Python-based ML models built from raw waveform and extracted signal data.
  • Develop advanced pattern recognition (APR) tools to identify fault patterns and early warnings.
  • Identify and extract meaningful features from vibration and electrical data.
  • Create detailed diagnostic reports including graphical outputs (e.g., frequency spectra, waveforms, and FEA models).
  • Maintain and structure diagnostic datasets using tools like Databricks or similar platforms.
  • Document recommendations for improving diagnostic tools, methods, and report formats.
  • Write reusable Python modules and scripts implementing diagnostic logic for specific asset types.
  • Validate models (e.g., waveform or FEA-based) and ensure robust documentation of assumptions and results.
  • Contribute to the platform’s knowledge base and technical manuals for future scalability and internal use.

Ideal Profile

  • Strong background in mechanical or electrical engineering, with hands-on experience in rotor dynamics, vibration analysis, or signal processing.
  • Proven Python development skills, especially in data analysis and modeling.
  • Experience with machine learning, signal processing, or data analytics tools.
  • Ability to work collaboratively across technical and research teams.
  • Strong analytical mindset, documentation habits, and an eye for continuous improvement.

If you’re passionate about combining engineering diagnostics, data science, and software development to build impactful tools for industrial reliability — we’d love to hear from you.

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