DATA PREPARATION

OVERVIEW

Industrial Data is Not Ready for Machine Learning

Industrial facilities generate large volumes of operational data from sensors, PLCs, SCADA systems, historians, and industrial equipment. However, this data is often noisy, incomplete, inconsistent, and recorded at irregular time intervals.

Machine learning models require clean, structured, and consistent datasets. Without proper data preparation, machine learning models would learn incorrect patterns and produce unreliable predictions.

ChronX Data Preparation transforms raw industrial signals into structured, reliable datasets that represent equipment behavior and operational performance.

CHALLENGES WITH INDUSTRIAL DATA

Industrial data presents several challenges that must be addressed before analytics and machine learning can be applied.

Common challenges include:

• Missing data points due to sensor or communication issues

• Noise and spikes that do not represent actual equipment behavior

• Signals recorded at different time intervals

• Different units and scales across sensors

• Multiple data sources across systems

• Raw signals that do not directly represent equipment performance

• Data gaps due to downtime or maintenance

• Irregular sampling frequencies

• Sensor drift and calibration issues

ChronX solves these challenges through a structured data preparation pipeline.

DATA INGESTION AND COLLECTION

Data Ingestion and Collection

ChronX collects industrial data from multiple data sources including PLCs, SCADA systems, industrial historians, IoT devices, sensors, and enterprise systems.

The platform supports:

• Time-series sensor data

• Event data

• Equipment status data

• Maintenance data

• Production data

• Environmental data

Data is collected and stored in a time-series data platform where it can be processed and prepared for analytics and machine learning.

INTERPOLATION AND MISSING DATA HANDLING

Interpolation and Missing Data Handling

Industrial data often contains missing values due to sensor failures, communication issues, or system downtime. Missing data must be handled before machine learning models can analyze time-series data.

ChronX uses interpolation techniques to fill missing data points and create continuous time-series signals.

Interpolation:

• Fills gaps in data

• Creates continuous signals

• Maintains time-series consistency

• Enables pattern recognition

• Prevents machine learning models from learning incorrect patterns

Interpolation methods may include linear interpolation, forward fill, backward fill, and time-based interpolation depending on the signal type.

NOISE REDUCTION AND SIGNAL FILTERING

Noise Reduction and Signal Filtering

Industrial sensor data often contains noise and random spikes that do not represent actual equipment behavior. Noise can negatively impact machine learning models and anomaly detection systems.

ChronX applies signal filtering and smoothing techniques to remove noise and improve signal quality.

Noise reduction includes:

• Spike removal

• Signal smoothing

• Moving average filtering

• Median filtering

• Low-pass filtering

• Outlier removal

This improves data quality and model accuracy.

TIME ALIGNMENT AND RESAMPLING

Time Alignment and Resampling

Industrial signals are often recorded at different time intervals. Some sensors may record data every second, while others record data every minute or hour.

ChronX aligns all signals into consistent time intervals using resampling techniques. This allows relationships between signals to be analyzed and machine learning models to learn patterns across multiple signals.

Time alignment includes:

• Resampling signals

• Aligning timestamps

• Synchronizing signals

• Creating uniform time intervals

• Aggregating high-frequency signals

AGGREGATION AND FEATURE GENERATION

Raw sensor signals do not always represent equipment performance directly. ChronX generates additional features and operational indicators from raw signals.

Feature generation may include:

• Averages

• Minimum and maximum values

• Rates of change

• Rolling averages

• Efficiency indicators

• Performance indicators

• Derived signals

• Equipment health indicators

These features help machine learning models understand equipment behavior more accurately.

WHY DATA PREPARATION MATTERS FOR AI MODELS

Why Data Preparation is Critical for Machine Learning

Machine learning models are only as good as the data used to train them. Poor data quality leads to inaccurate models, false alarms, and unreliable predictions.

Data preparation is critical because it:

• Improves model accuracy

• Reduces false alarms

• Enables pattern recognition

• Improves anomaly detection

• Enables failure prediction

• Creates reliable datasets

• Improves model stability

• Enables scalable AI deployment across equipment

ChronX

Data Preparation Making Industrial Data AI-Ready

OVERVIEW

Industrial Data is Not Ready for Machine Learning

CHALLENGES WITH INDUSTRIAL DATA