Predictive Maintenance

13 hours ago

Edmonton, Alberta, Canada Small busines ideas Full time

Job Overview

Responsible for building predictive maintenance models based on equipment operation data (sensors, logs, historical maintenance records, etc.) to proactively identify equipment failure risks, optimize maintenance strategies, reduce downtime and maintenance costs, and improve equipment reliability and asset utilization.

Main Responsibilities

Data Acquisition and Processing

Integrate equipment data from sensors (vibration, temperature, current, pressure, etc.), SCADA, MES, and CMMS.

Perform data cleaning, feature engineering, and data quality assessment.

Predictive Model Development

Build equipment health assessment, anomaly detection, and remaining useful life (RUL) prediction models.

Apply statistical analysis, machine learning, or deep learning methods (such as regression, time series models, anomaly detection).

Fault Analysis and Diagnosis

Analyze equipment failure modes (FMEA/FMECA).

Perform root cause analysis by combining mechanistic and data models.

Model Deployment and Optimization

Deploy the model to the production environment (edge/cloud).

Continuously monitor model performance and iteratively optimize.

Business Collaboration

With Equipment engineers and maintenance teams collaborate to translate forecast results into actionable maintenance strategies.

Support maintenance plan optimization and spare parts management decisions.

Documentation and Reporting

Output technical documentation, analysis reports, and visual dashboards.

Clearly communicate analysis results to technical and non-technical teams.

Job Requirements Basic Requirements

Bachelor's degree or above in Mechanical Engineering, Electrical Engineering, Automation, Computer Science, Data Science, or related fields.

2+ years of experience in industrial data analysis, equipment maintenance, or related fields (educational requirements may be relaxed for senior positions).

Skills Requirements

Familiar with at least one programming language: Python /R / MATLAB

Proficient in machine learning or statistical analysis methods (regression, classification, clustering, anomaly detection, time series analysis, etc.)

Familiar with the characteristics of industrial equipment and sensor data

Strong data analysis and problem breakdown capabilities

Bonus points

Experience in predictive maintenance and equipment health management (PHM) projects

Familiar with vibration analysis, signal processing, and frequency domain analysis

Experience in industrial internet, digital twins, and edge computing

Familiar with common industrial protocols (OPC UA, Modbus, etc.)

Experience with cloud platforms (AWS / Azure / Alibaba Cloud, etc.)

Soft skills required

Excellent communication skills, able to translate technical results into business value

Strong learning ability and interdisciplinary collaboration ability

Engineering thinking and continuous improvement mindset

Work achievements and KPI examples

Reduction rate of unplanned equipment downtime

Prediction accuracy / Early warning time

Maintenance cost savings

Model deployment and stable operation status

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