News release | August 2026

ZRAY has started equipping all automatic case erectors with a standard IoT sensor package that feeds a cloud-based predictive maintenance and analytics platform. From October 2026, every new machine ships with vibration, temperature, air-flow, and cycle-count sensors — and existing machines can be retrofitted with the same kit in one day.

What the Sensor Package Measures

The IoT package monitors the failure modes that actually drive case erector downtime:

The sensors sample continuously; the onboard edge processor aggregates 1-minute statistics and sends them to the ZRAY analytics cloud over the machine’s existing network connection (no SIM card or extra gateway required).

How Predictive Maintenance Works in Practice

1. Baseline and Drift Detection

Each machine builds a baseline profile in its first 200 operating hours — normal vibration amplitude, normal air-flow signature, normal temperature curve. The cloud platform then flags drift beyond a threshold, e.g.:

• Vibration amplitude up 15% on the forming axis → bearing inspection recommended within 2 weeks.
• Air flow down 10% at constant cycle rate → check filter and valve bank.
• Vacuum level down 12% → vacuum cups likely worn, check for cracks.

2. Early Warnings, Not Alarms

The system sends advisory alerts before failure, with a suggested action and a time window. A typical sequence:

Day 1: “Vibration on forming axis trending up” (advisory) → Day 9: “Inspect forming station bearings” (recommended within 7 days) → Day 14: “Replace bearing #2 — wear confirmed by spectral peak at 120 Hz” (planned replacement).

The maintenance team schedules the replacement during a planned stop instead of reacting to a bearing failure mid-shift.

3. Fleet-Level Analytics

For plants running several ZRAY machines — or a corporate team managing multiple sites — the platform provides:

• OEE dashboards per machine and per line.
• Comparison across machines: same model, same board grade, different plant — why does machine A run 5% fewer jams than machine B?
• Spare parts forecasting: the platform aggregates wear-counter data across the fleet and flags parts that will be needed next month, feeding the CMMS and parts inventory.

Data Ownership and Security

• Machine data is customer-owned; ZRAY analytics runs on the customer’s chosen destination (ZRAY cloud, plant server, or any OPC UA / MQTT endpoint).
• The sensor data channel uses TLS and the same role-based access model as the Industry 4.0 control architecture.
• Data is anonymized at the fleet level unless the customer opts into shared benchmarks.

Compatibility

The IoT sensor package is standard on all new automatic case erectors — single-piece case erector, three-piece case erector, and double-head high-speed case former. Retrofit kits are available for machines installed since 2022, installed in one working day by a ZRAY technician.

Cost Logic

Predictive maintenance is not a feature — it is a cost line. In ZRAY field data across beverage and e-commerce plants:

• Unplanned erector failures cost USD 300–900 per hour of line downtime (filler idling, crew waiting, product held).
• Predictive replacement of wear parts costs 10–25% of the reactive equivalent (no emergency shipping, no overtime, no collateral damage from the failed part).
• Typical payback on the sensor package: 3–6 months for a line running 2+ shifts.

Availability

IoT sensors are standard on all new orders and available as a retrofit now. If you run ZRAY machines and want the fleet dashboard, request the analytics platform trial — we will set up your machines in the cloud within a week.

Request the IoT predictive maintenance trial or message us on WhatsApp at +86 13681839278.