首页|Study Results from Purdue University Broaden Understanding of Machine Learning ( Bearing Anomaly Detection In an Air Compressor Using an Lstm and Rnn-based Machi ne Learning Model)
Study Results from Purdue University Broaden Understanding of Machine Learning ( Bearing Anomaly Detection In an Air Compressor Using an Lstm and Rnn-based Machi ne Learning Model)
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Current study results on Machine Learn ing have been published. According to news originating from West Lafayette, Indi ana, by NewsRx correspondents, research stated, "Smart systems such as data-driv en machine health monitoring are emerging as powerful technology for advanced ma nufacturing as a result of the availability of low-cost sensors, wireless commun ication, and advances in Machine Learning (ML) and Artificial Intelligence (AI). Predictive maintenance (PdM) has become increasingly popular in manufacturing, which can identify approaching failures, determine root causes of operation anom alies, estimate the current health state of a system, and predict the future sta te and time when a component will fail in the absence of an intervention." Financial support for this research came from Wabash Heartland Innovative Networ k. Our news journalists obtained a quote from the research from Purdue University, "One weakness of many past studies is the lack of run-to-failure data from an ac tual production environment. This paper presents run-to-failure data for the air compressor of an injection molding machine. A Long Short-Term Memory (LSTM) Rec urrent Neural Network (RNN) is proposed to detect bearing faults in the air comp ressor, which can capture the long-term dependencies without losing the capabili ty to identify local dependencies. The model achieves a 97.4% of p rediction accuracy (95.3% of overall accuracy)."
West LafayetteIndianaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPur due University
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