{"id":331,"date":"2025-01-14T14:35:35","date_gmt":"2025-01-14T13:35:35","guid":{"rendered":"https:\/\/dejonge.ai\/anomaly-detection\/"},"modified":"2026-04-13T20:45:49","modified_gmt":"2026-04-13T18:45:49","slug":"anomaly-detection","status":"publish","type":"page","link":"https:\/\/dejonge.ai\/en\/use-cases\/anomaly-detection\/","title":{"rendered":"Fingerprinting"},"content":{"rendered":"
\r\n\t\r\n<\/svg><\/div>

Fingerprinting<\/h1>
<\/div><\/div><\/div><\/div><\/div><\/div><\/section>
<\/i>Use cases<\/span><\/a><\/div><\/div><\/div><\/div>

Every machine has a handwriting. EdgeBrain reads them. <\/span><\/span><\/h2>
\"\"<\/div>
<\/div>

The task<\/h3>\n

Production systems continuously generate raw data: Vibration signals from bearings and drives, acoustic emissions during machining, thermographic images of heat distribution, RGB images of surfaces. Every machine, every tool, every process step leaves a characteristic imprint in the signal. In normal operation, this imprint is stable and reproducible. If it deviates, a problem becomes apparent – often hours or days before it becomes visible. <\/p>\n

The aim is to automatically learn this individual fingerprint, monitor it permanently and detect the smallest deviations at an early stage.<\/p>\n

The challenge<\/h3>\n

Raw data from time series, images and audio signals have fundamentally different structures. A vibration signal delivers thousands of samples per second, a thermographic image millions of pixel values, an acoustic sensor a broadband frequency spectrum. These data volumes cannot be directly compared with each other. Classic threshold value monitoring fails as soon as operating conditions vary: Speed changes, load changes and temperature influences change the raw signal without an error being present. Conventional approaches therefore either produce false alarms or overlook real drifts.<\/p>\n<\/div><\/div>

<\/div>

The solution<\/h3>\n

EdgeBrain translates each data type into a compact, comparable fingerprint. Characteristic features in the frequency range are extracted from the vibration signal (FFT profiles, envelopes, kurtosis). Texture and distribution features are created from images. Acoustic signals are converted into spectrograms and compressed. The result is a high-dimensional but compact feature vector – the fingerprint. <\/p>\n

In learning mode, EdgeBrain records a baseline: the typical fingerprint of a healthy, correctly running machine is recorded under real operating conditions. From this point on, EdgeBrain compares each new fingerprint with the baseline. Deviations are output as an anomaly score. If the score rises above an adaptive threshold value, the system sounds an alarm – even before a conventional sensor would report a failure. <\/p>\n

Drifts are detected by EdgeBrain chaining the fingerprints over time and making slow shifts in the feature space visible. In this way, the system distinguishes between sudden anomalies (impact damage, foreign contact) and gradual wear (bearing degradation, tool wear). <\/p>\n

Key technical data<\/h3>\n\n\n\n\n\n\n\n\n\n\n
Parameter<\/th>\nValue<\/th>\n<\/tr>\n<\/thead>\n
Input data<\/td>\nTime series (vibration, acoustics), images (RGB, thermal), pressure curves, currents<\/td>\n<\/tr>\n
Fingerprint methods<\/td>\nFFT profiles, spectrograms, texture features, statistical moments<\/td>\n<\/tr>\n
Detection<\/td>\nAnomaly score (real-time), drift trend monitoring (time-based)<\/td>\n<\/tr>\n
Learning mode<\/td>\nUnsupervised baseline recording during operation<\/td>\n<\/tr>\n
Latency<\/td>\n< 100 ms from sensor to alarm<\/td>\n<\/tr>\n
Infrastructure<\/td>\nCompletely local on EdgeBrain, no cloud connection required<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The added value<\/h3>\n

Fingerprinting with EdgeBrain transforms raw data into machine intelligence. Systems for which there is no fault model and no historical failure data can still be monitored – because the system learns the normal state itself. Maintenance intervals are decoupled from the rigid calendar schedule and are based on the actual condition of the machine. Unplanned downtime is reduced, spare parts costs fall and system availability increases measurably.<\/p>\n<\/div><\/div>

<\/div>
Contact us<\/span><\/i><\/a><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"

Generate a unique machine fingerprint from vibration, acoustic and image data and detect anomalies and drifts in real time<\/p>\n","protected":false},"author":3,"featured_media":1168,"parent":333,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-331","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nAI fingerprinting with Edge AI in real time | de Jonge<\/title>\n<meta name=\"description\" content=\"AI fingerprinting in real time: Recognize patterns, deviations and identities directly in the data stream - locally, reliably and securely.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dejonge.ai\/en\/use-cases\/anomaly-detection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI fingerprinting with Edge AI in real time | de Jonge\" \/>\n<meta property=\"og:description\" content=\"AI fingerprinting in real time: Recognize patterns, deviations and identities directly in the data stream - 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