Anomaly Detection
Anomaly Detection - Related Videos
03:40
Three-Dimensional Printing of a Complex Aortic Anomaly
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Cited by 3 •
2018
Here, we present a protocol to use three dimensional printed models for pre-operative planning and intra-operative reorganization of complicated vascular locations when handling a congenital aortic anomaly.
09:10
Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce
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Cited by 17 •
2010
This protocol describes a simple adhesive-tape-based approach for sampling of tomato and other fresh produce surfaces, followed by rapid whole cell detection of Salmonella using fluorescence in situ hybridization (FISH).
09:00
Detection of Protein Ubiquitination
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Cited by 96 •
2009
Ubiquitination is a key posttranslational modification carried out by a set of three enzymes. Mutations of genes involved in this modification are associated with many different human diseases. Here, we describe protocols to detect protein ubiquitination in cultured cells in vivo and test tubes in vitro.
09:08
Detecting Reactive Oxygen Species
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2023
Reactive oxygen species are chemically active, oxygen-derived molecules capable of oxidizing other molecules. Because of their reactive nature, there are many deleterious effects associated with unchecked ROS production, including structural damage to DNA and other biological molecules. However, ROS can also be mediators of physiological signaling. There is accumulating evidence that ROS play significant roles in everything from activation of transcription factors to the mediation of...
13:20
Detection of Bacteria Using Fluorogenic DNAzymes
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Cited by 17 •
2012
We have recently reported a novel approach for generating fluorogenic DNAzyme probes that can be applied to set up a simple, "mix-and-read" fluorescent assay for bacterial detection. These special DNA probes catalyze the cleavage of a chromophore-modified DNA-RNA chimeric substrate in the presence of crude extracellular mixture (CEM) produced by a specific bacterium, thereby translating bacterial detection into fluorescence signal generation. In this report we will describe key experimental...
FAQs
The process first models normal operation from sensor data or historical measurements. This reference pattern provides the basis for evaluating later observations, events, or system behaviors. When new measurements diverge from the modeled baseline, the system can flag them for engineering review, helping distinguish ordinary operating variation from changes that may indicate faults, threats, or emerging failures.
Statistical thresholds flag measurements that move beyond an expected range, while residual analysis examines the difference between an observed value and a modeled or expected value. Machine-learning algorithms provide another way to identify unusual patterns in the data. These approaches support the same investigative goal but emphasize different forms of deviation and pattern recognition.
Residual analysis focuses attention on the gap between measured behavior and what the system model or expected pattern predicts. A changing or unusually large residual can reveal that the system no longer behaves as anticipated, even when individual measurements may not appear obviously abnormal. Engineers can then investigate whether the change reflects a fault, developing failure, or another system condition.
An engineering workflow begins by gathering sensor data or historical measurements that represent system operation, followed by modeling the normal pattern. New observations are then evaluated with statistical thresholds, residual analysis, or machine-learning algorithms. Flagged deviations become targets for investigation, allowing engineers to examine possible causes and determine whether corrective action, maintenance, or further monitoring is appropriate.
Applications include monitoring industrial equipment, identifying manufacturing defects, detecting network intrusions, and supporting predictive maintenance. The relevant data may describe equipment behavior, production results, or network activity. In each case, unusual patterns provide an early signal for closer examination, helping teams respond to potential problems before they develop into more significant reliability, quality, or security concerns.
By revealing unusual conditions early, anomaly detection gives engineers evidence that system behavior has changed and may require investigation. In predictive maintenance, those signals can support decisions about when equipment should receive attention. More broadly, the resulting information can contribute to improved reliability, safety, quality control, and operational efficiency while helping engineers explore the causes of observed changes.