Predictive and Prescriptive maintenance of manufacturing industry with machine learning

This has enabled predictive and prescriptive scheduled maintenance of our component manufacturing process reducing sudden & unplanned downtime/ disruption & optimizing our factory capacity, consumables, labor and cost..The platform integration of AI/ML, a time-aware runtime system and flexible authoring tool allowed a much better prediction of process and equipment issue and enabled precise maintenance of our equipment assembly avoiding sudden & unplanned downtime / avoiding intermittent faulty manufacturing minimizing revenue loss.Major benefits we realized:Increased yieldBoosted worker productivityReduced unplanned downtimeOptimized profitabilityRapid implementationQuick scalabilityOverview of ML algorithms:What is Anomaly detection? :Anomaly detection is about finding patterns (such as outliers, exceptions, peculiarities etc.) that deviate from expected behavior within datasets(s) — therefore it can be similar to noise removal or novelty detection..A pattern detected with anomalies are actually of interest, noise detection can be slightly different, because the sole purpose of noise detection is removing those noise..As with most data science projects, the ultimate goal of anomaly detection is not just an algorithm or working model..Instead, it’s about the value of the insight the anomalies/outliers provide — i.e..for the business money saved from preventing equipment damage..In the manufacturing sector — we want to proactively achieve predictive & prescriptive maintenance using anomaly detection before it actually damage the equipment..This would pre-alert and enable “scheduled maintenance” avoiding sudden downtime which usually leads to heavy revenue lossSupervised vs Unsupervised:There are two primary architectures for building anomaly detection systems:Supervised anomaly detection — which we can use if we have labeled dataset where we know whether or not each data point is normal or notUnsupervised anomaly detection — where the dataset is unlabeled i.e..whether or not each data point is an anomaly is unreliable or unknownOur ML Pipeline:Our state-of-the art ML software framework performs real time sensor data acquisition from sensors strategically placed in various parts of our manufacturing assembly..All in real-time, our software framework performs statistical feature extraction from the acquired sensor signals, derive the principal components, and detects anomalous clusters (data points) using supervised and unsupervised learning techniques, performs time series preemptive prediction on the sensor signals along with confidence interval guard bands and detects extreme vibrations..Following is an overview:A Sample Unsupervised Algorithm detecting various clusters:Mean Shift clustering aims to discover clusters in a smooth density of data points..It is a centroid based algorithm; it starts by considering each data point as a cluster center.. More details

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