In complex manufacturing environments, subtle changes in machine behavior often precede breakdowns, quality defects, or safety issues. AI4M’s Anomaly Detection in Time Series solution enables manufacturers to uncover these hidden early warnings by continuously analyzing patterns in high-frequency machine data.
1. Servo Current Patterns – Predicting Laminate Jamming
In vertical and horizontal form-fill-seal (VFFS/HFFS) machines, servo-driven
jaws are responsible for forming seals with precise force and timing. AI4M’s
anomaly detection identifies early-stage jamming of packaging laminates by
spotting irregular spikes, vibration patterns, or torque drift in the servo
current time series — often 10–30 seconds before a mechanical jam actually
occurs. This enables automated alerts or pre-emptive machine stops before any
damage or downtime.
3. Subtle Defects in Batch or Continuous Processes
In chemical and metal processing, time series from valve positions, pump loads,
flow rates, and energy consumption can reveal inefficiencies or deviations long
before they become quality incidents. AI4M’s models continuously learn from this
data to detect drifts, surges, or prolonged idle behavior, improving reliability
and process stability.
2. Leakage Prediction in Liquid & Paste Sachet Machines
Leaky sachets in liquid or paste filling lines often arise from a complex
interplay of seal pressure, material wrinkles, fill temperature, or dwell time.
By monitoring temperature and pressure signatures over time — and learning
normal baselines — our system can detect deviations that precede seal failure.
This gives line operators critical time to adjust sealing parameters or halt the
line for inspection, drastically reducing rework and customer complaints.
- Unsupervised learning for unknown or evolving faults
- Early warning system – acts before alarms or stoppages
- Works with standard PLC or SCADA data
- Integrates into existing historian or MES systems
AI4M’s anomaly detection engine acts like a digital “nervous system” for your
machines—constantly listening for signs of stress, misalignment, or emerging defects,
and surfacing them before they affect production.