Deep Learning (3)
This use case summarizes findings of a health monitoring study using empirical vibration and temperature data to build a predictive maintenance model. Sensors are placed in four different positions on the housing surface of three running motors at different health stages to study the model performance and its robustness with respect to sensor mounting and various operating conditions. Tens of thousands of data segments were processed and used to extract features and build supervised and unsupervised classification algorithms. A feed forward Neural Network was deployed to classify signals (unseen before by the network) from these 3 motors. Preliminary results look promising with 99.2 % classification accuracy. It is also worth to note the algorithm robustness with respect to sensor mounting.
Pattern recognition is the process of recognizing patterns by using a Machine Learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Pattern recognition is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. Predictive analytics in data science work can make use of pattern recognition algorithms to isolate statistically probable movements of time series data into the future. In a technological context, a pattern might be recurring sequences of data over time that can be used to predict trends, particular configurations of features in images that identify objects, frequent combinations of words and phrases for natural language processing (NLP), or particular clusters of behaviour on a network that could indicate an attack — among almost endless other possibilities. In IT, pattern recognition is a branch of Machine Learning that emphasizes the recognition of data patterns or data regularities in a given scenario. Pattern recognition involves classification and cluster of patterns.
Speaker Diarization is the process of automatically annotate an audio stream with speakers’ labels. Generally, it is a task of determining the number of speakers who are active and their utterance duration in an audio file.