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predictive-maintenance

predictive maintenance

GE Predix

SystemTechniques · 9Primitives · 32 predictive-maintenance Gradient-boosted decision trees (GB…machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.›Vibration spectral analysissignal-processing · medium: FFT spectra, envelope demodulation and order tracking to find bearing and gear faultsRemaining useful life estimationstatistics · medium: Degradation and survival models predicting time to failure from condition dataControl charts (Shewhart, CUSUM, EW…statistics · medium: Estimate normal behaviour from healthy data, then signal when values leave limits (Shewhart) or small deviations add up (CUSUM, EWMA); tune by average run length.›Time-series anomaly detectionstatistics · medium: Predict the expected value from a seasonal baseline, scale the residual by a robust spread (MAD), threshold at a chosen false-alarm rate, then merge flags into incidents.›Columnar storagestorage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.›Rollup aggregationstorage · medium: Fold raw events into one mergeable partial aggregate per time bucket and dimension key, then merge fine buckets into coarser ones, so long ranges read few rows and stay exact.›Fast Fourier transformnumerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.›Gorilla XOR compressioncompression · low: Time-series codec: delta-of-delta timestamps plus XOR of consecutive float bit patterns, storing only changed bits; about 1.4 bytes per point.›Opc uaembedded · low: OPC UA industrial protocol: address space, subscriptions and monitored items, security modesEvaluation metricsmachine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.›PCA and SVDmachine-learning · low: Find orthogonal directions of maximum variance by an SVD of the centred data; randomized SVD gets the top k components in a few passes.›Mqtt protocolnetworking · low: MQTT pub/sub: topics, QoS 0/1/2, retained messages, sessions and keepaliveQuantile sketchesdata-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histogramsLoss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›Regularizationmachine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.›IIR and FIR filterssignal-processing · low: Two ways to shape a sampled signal's spectrum: FIR (finite taps, always stable, exact linear phase) or IIR (feedback, a few coefficients, cheap but phase-distorting).›Peak detectionsignal-processing · low: Robust peak and event detection in noisy signals with adaptive thresholds and refractory periodsShort-time Fourier transform and sp…signal-processing · low: Slide a window over a signal and FFT each frame to get a time-frequency picture; the front end of audio ML, vibration and radar analysis.›Recurrent networksmachine-learning · low: GRU and LSTM cells for streaming sequence modelsExponential smoothing (ETS, Holt-Wi…statistics · low: Forecast by keeping a few running states (level, trend, seasonal) updated by a fraction of each forecast error; O(1) memory per series and a strong baseline.›Robust statisticsstatistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.›Sliding-window counterssystems · low: Count events in the last W seconds with a ring of sub-bucket counters; the two-bucket weighted estimate gives rate limits in 16 bytes per key.›+ 2 more techniques+ 16 more primitives

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