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Detecting bearing failure

Every rotating machine depends on its bearings. When one starts to fail, vibration patterns shift — subtle at first, unmistakable by the end. Catching that shift early is the difference between a planned replacement and a catastrophic breakdown.

This is a real bearing failure from the NASA IMS Bearing Dataset  — one of the most cited run-to-failure experiments in predictive maintenance. A bearing degrades from healthy to catastrophic over 7 days while accelerometers sample every vibration at 20 kHz.

Each 10-minute snapshot is condensed to window statistics — RMS, kurtosis, and more — using the same winkComposer building blocks that would run at the edge in production. A winkComposer flow then scores the bearing’s health from these statistics in real time.

Drag the slider and watch it detect the failure before it happens.

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What You’re Seeing

The chart shows vibration RMS (cyan) — the overall energy in the signal — tracked by an exponentially smoothed mean (lavender dashed). The filled envelope around the mean is the peak-tracking vibration envelope — tight when the bearing is healthy, widening as degradation introduces new vibration modes.

For about the first day the system shows Learning — it is observing the equipment’s baseline vibration automatically, with no manual threshold tuning required.

The bearing holds steady for about 3.75 days. RMS and kurtosis stay normal, and the verdict reads Healthy. Around day 4, evidence builds across multiple signals at once — energy spikes, a rising trend, and falling signal clarity — and the verdict jumps straight to Critical. The change-point markers (vertical dashed lines) visually confirm the shift.

The key signals table shows each signal’s intensity and persistence — distinguishing sustained degradation (Continuous) from transient spikes (Intermittent). This is the first question maintenance engineers ask: is this real or a false alarm?

What you’re watching is a P-F curve — the trajectory from the point where a fault becomes detectable (P) to functional failure (F). The entire discipline of predictive maintenance exists to maximise the interval between P and F.

How It Works

One flow, six building blocks — each contributes a different perspective on bearing health:

twStatsextracts features from raw 20 kHz vibration(offline here; live in production)esStatssmooths RMS and tracks the ±σ envelope→ the bearing’s volatility signaturermsTrenddetects rising vibration energy→ the degradation ratekurtTrenddetects rising impulsiveness→ often the first sign of a defectrmsCPDsignals a level shift in RMS→ visual marker on the chartkurtCPDsignals a level shift in kurtosis→ corroborates the RMS change-pointbearingHealthself-calibrates, then fuses 4 signals into a verdict→ verdict + intensity × persistence

ES Stats smooths the raw RMS signal and tracks the vibration envelope — the lavender band on the chart. It also computes the z-score (how far the current reading deviates from normal) and signal-to-noise ratio (SNR), which drops as the bearing’s behaviour becomes erratic.

RMS Trend watches whether the smoothed RMS is climbing consistently — the degradation rate. Only a rising trend with sufficient confidence passes through to the assessment.

Kurtosis Trend does the same for impulsiveness — often the first sign of a defect.

RMS Change-Point Detection and Kurtosis Change-Point Detection run Page-Hinkley  tests that fire when the signal shifts to a new level — the vertical markers on the chart. When both fire at roughly the same time, two independent physical signals confirm the same shift.

The health assessment at the end of the pipeline fuses four signals into one verdict:

  • Energy spikes — z-score from ES Stats (current deviation from the running average)
  • Impact events — raw kurtosis (spiky vibration patterns)
  • Energy trend — trend confidence, gated to the rising direction only (zero when stable, regardless of confidence)
  • Signal clarity — SNR from ES Stats (drops as behaviour becomes erratic)

During the first ~1.2 days, the node learns this bearing’s normal vibration signature and calibrates a verdict scale from it — no manual threshold tuning per asset. Evidence for each signal then builds while the signal is outside its normal range and fades as it returns. Combined evidence determines the verdict — Healthy, Monitor, Degraded, or Critical — and the recommended action. Each signal’s intensity and persistence appear alongside.

References

  • Qiu, H., Lee, J., Lin, J. & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, 289, 1066–1090. doi:10.1016/j.jsv.2005.03.007 
  • Page, E.S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115. doi:10.2307/2333009 
  • Dataset: NASA IMS Bearing Data  — University of Cincinnati, 2004. Test 2, Bearing 1: inner race defect, ~164 hours run-to-failure. Four bearings under 6,000 lb radial load at 2,000 RPM. Accelerometers captured 20,480 samples at 20 kHz every 10 minutes (982 snapshots).

Next Steps

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