MACHHUB HMI
HMI Replaced
Half-day → 90 min
Breakdown Resolution
OEE + CM + Historian
Capabilities Built-in
3 at launch
Lines with Data Historian
The Challenge
The OEM builds semi-automatic and fully automatic liquid chemical filling lines for industrial and household chemical customers. Their machines were shipped with a conventional touchscreen HMI for operator control and a paper-based maintenance checklist. Customers had no visibility into machine performance between service visits, and the OEM had no data from the field to understand how machines were actually being used or where failures occurred most frequently. As customers grew more sophisticated, requests for OEE data, fill count logs, and detailed fault history were increasing — requirements the current HMI architecture could not satisfy without significant custom development on each project. When machines broke down, service engineers arrived on-site with no prior context — no data on what the machine was doing before the fault, making diagnosis slow and unpredictable.
The Solution
Integrated MACHHUB as a standard platform across the filling machine range, replacing the standalone touchscreen HMI with a MACHHUB-powered operator interface running on an industrial panel PC. Three capabilities were built as standard machine features: OEE Monitoring — capturing fill cycle counts, planned vs actual output, and downtime reasons categorised by the operator at the machine; Condition Monitoring — tracking filling pump motor current draw and nozzle manifold pressure via IO-Link sensors connected through a Modbus TCP IO-Link master, with alerts when readings deviate from the baseline established during FAT; and a continuous on-machine data historian — all sensor readings, alarms, and operator actions are recorded locally in MACHHUB on the machine itself. When a fault occurs, the OEM's service team downloads the machine's historical data and uses it to replay the timeline of events leading up to the breakdown. This machine history, combined with AI-assisted diagnostic tools, allows the service team to identify root causes significantly faster than blind on-site investigation.
The Result
The first three machines shipped with the new platform were delivered to a household chemical manufacturer in Selangor. Within the first month, a gradual increase in filling pump current on Line 2 was detected by the condition monitoring alerts — the OEM's service team downloaded the machine history, confirmed the trend, and replaced a partially worn pump seal during a planned visit before it caused a breakdown. When Line 3 experienced an unplanned stop two months later, the service engineer downloaded the pre-fault data log before travelling to site, used AI-assisted analysis to identify a nozzle pressure anomaly pattern from the hour preceding the fault, and arrived with the correct replacement part — resolving the breakdown in under 90 minutes instead of the usual half-day investigation.
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