Flowchart for diagnosing lift noise: separate airborne vs structure-borne, then measure, correlate, take corrective action, and verify.
A newly renovated lift should not suddenly produce “abnormal” noises. When it does, residents often describe it as mysterious because the sound can be intermittent, hard to localise, and more noticeable at certain times of day. A recent Jurong East case highlights the typical pattern: repeated resident reports, operational disruption, and an active investigation while stakeholders determine whether the root cause is vandalism, a fault, or a workmanship issue.
From an acoustic engineering perspective, this kind of case is rarely mysterious. It is usually one of two mechanisms:
In both cases, the fastest route to resolution is not debate. It is evidence.
After refurbishment or component replacement, the noise profile can change due to:
Most “mysterious lift noise” investigations stall because they rely on phone videos and subjective descriptions, without separating the two pathways:
If you do not separate these, you risk applying the wrong fix—often at significant cost—and the issue returns.
Ask residents and building staff to log:
This creates the correlation backbone.
The most valuable data point is not a decibel number—it is time alignment:
Once you have synchronization, the root cause short-list becomes much smaller.
A minimal, effective package typically includes:
Outcome: you stop guessing and start attributing.
If airborne dominates, typical corrective actions focus on:
If structure-borne dominates, typical corrective actions focus on:
A) Immediate operational decision
B) Evidence capture
C) Escalation trigger
D) Fix selection
Abnormal lift noise quickly becomes a trust and safety perception issue. Even if the underlying cause is not dangerous, uncertainty drives escalation. A structured engineering workflow reduces:
Geonoise Asia supports independent diagnostics for building noise and vibration problems, including lift-related airborne and structure-borne mechanisms. The objective is decision-grade evidence: identify the dominant pathway, correlate noise with operational events, and define corrective actions that close the issue efficiently.
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