Technical insights on bearing monitoring, forensic evidence capture, and industrial sensor technology.
Cloud-only bearing monitoring architectures have real limitations: latency, bandwidth costs, connectivity dependency, and data sovereignty. Learn when edge AI processing provides genuine advantages and how hybrid architectures deliver the best of both.
Read more →Classification societies like DNV and Lloyd's increasingly require continuous bearing condition monitoring. Learn how dual-mode sensors meet marine monitoring requirements while providing forensic evidence for warranty disputes and insurance claims.
Read more →Standard condition monitoring data falls short in warranty disputes, insurance claims, and compliance investigations. Learn what makes bearing failure evidence forensic-grade and why it matters when determining who pays.
Read more →Predictive maintenance sensors and forensic failure recorders serve different purposes, but deployed together they capture a wider band of information than either system alone — from weeks of degradation context to the high-fidelity physics of the failure event itself.
Read more →Predictive maintenance systems are designed to prevent bearing failures, not document them. When failure occurs despite prediction, the data these systems capture is structurally inadequate for forensic investigation and dispute resolution.
Read more →Standard bearing monitoring data is useful for maintenance planning, but it cannot function as evidence in a failure dispute. Tamper-evident data — architecturally sealed at capture and verifiable by any party — changes the dynamics of bearing failure investigations.
Read more →When a critical bearing fails, the mechanical failure is rarely the most expensive outcome. The dispute about why it failed is. Forensic bearing evidence — high-fidelity, tamper-evident physical data captured at the moment of failure — changes the economics of bearing failure disputes.
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