Fault Detection

Beleuchtungsinvariante Kontourerkennung - Beleuchtungsinvariante Bewegungsdetektion - Hyperbeldetektion - Informationsraumanalyse - CI basierte Identifikatoren - Datenkategorisierer - Alarmierungsroutinen - Maschinenüberwachung - Harmonischenanalyse - DLS-Analysatoren - FD-Analysatoren - CI-Alarmierungsroutinen - Trendanalyse - neuronale Qualitätskontrolle - Sensorfusion - neuronale Prädiktoren - Analyse biologischer Signale - neuronal basierte Identifikatoren - akustische Mustererkennung - Trendanalyse - CI basierte Personenkategorisierung

₪ Fault status monitoring in distributed hybrid sensor systems, shown here with the status analysis of a supply network

Many companies today have extensive databases of their applications and processes. Only who evaluates this data automatically according to company-relevant criteria? How do you eliminate natural trends such as meteorological fluctuations, irrelevant interference that occurs again and again (randomly or periodically) and sensor-related peculiarities as well as sensor-related deviations in the data sets without also deleting the actual errors, malfunctions or anomalies of a system?

IngB has thought about this and dared to combine the traditional with the latest technology in its products in order to identify and mark company-relevant information from extensive (old and new) data. Everything according to the approach: statistics and computer intelligence do not have to conflict, but should complement each other!

Put together in a problem-oriented manner, a system was created which – once task-specifically conditioned – automatically searches your databases for the events that are really of interest to you, builds up problem-relevant error statistics, is continuously updated from the first use and thus relieves your employees of only having to look at the “really” relevant need to take a closer look at the data.

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