Bearing defect frequencies are the four characteristic vibration frequencies a rolling-element bearing generates when a specific part is damaged: BPFO (ball pass frequency, outer race), BPFI (ball pass frequency, inner race), BSF (ball spin frequency), and FTF (fundamental train frequency, the cage). Each is a fixed, non-integer multiple of shaft speed set by the bearing's geometry.

These four numbers are the reason a vibration analyst can point at a spectrum and say "outer race spall on the drive-end bearing" weeks before the bearing runs hot. This guide covers what each frequency means, how to calculate all four from a datasheet, a fully worked example, and a five-step workflow for turning a spectrum into a diagnosis. It sits underneath the broader predictive maintenance and condition-based maintenance programs that vibration analysis feeds.

What are the four bearing defect frequencies?

Each frequency corresponds to one surface inside the bearing. When a rolling element rolls over a defect on that surface, it produces a small impact; those impacts repeat at a rate fixed by geometry and shaft speed.

Bearing anatomy and the four defect frequenciesWhich part makes which frequencySHAFTOUTER RACE → BPFOROLLING ELEMENT → BSFINNER RACE → BPFICAGE / TRAIN → FTFBPFO + BPFI = number ofrolling elements (a sanity check)
The four surfaces and their frequencies. Damage on each surface produces impacts at a different, predictable rate, the fingerprint that lets you localize a fault without opening the housing.

How do you calculate bearing defect frequencies?

Each frequency is computed from four geometry values plus shaft speed. The four geometry values come from the bearing manufacturer's datasheet or catalog:

Let n be shaft speed in Hz (RPM ÷ 60). The standard formulas, published in the same form by every major bearing manufacturer, are:

FrequencyFormulaWhat a peak means
BPFO(N/2) × n × [1 − (d/D)cosθ]Defect on the outer race
BPFI(N/2) × n × [1 + (d/D)cosθ]Defect on the inner race
BSF(D/2d) × n × [1 − (d/D)²cos²θ]Defect on a rolling element
FTF(1/2) × n × [1 − (d/D)cosθ]Cage wear or damage
The four defect-frequency formulas. All share the same geometry ratio d/D, which is why the numbers are related, and why BPFO + BPFI always equals N×n.

Two properties are worth internalizing. First, these are orders of running speed, multiply the bracketed value by shaft speed and you get frequency in Hz. Second, they are almost never whole numbers. A gear mesh or a loose foot lands on integer multiples of running speed; a bearing defect lands at 3.58× or 5.42×. That non-integer signature is how you tell a bearing fault from everything else on the shaft.

A worked example

Take a common deep-groove ball bearing with illustrative geometry: N = 9 elements, ball diameter d = 7.94 mm, pitch diameter D = 39.0 mm, contact angle θ = 0°, running at 1,750 RPM (n = 29.17 Hz). The ratio d/D = 0.204.

Sanity check: BPFO + BPFI = 3.58 + 5.42 = 9.0 orders = N. If your calculated pair does not sum to the ball count, you entered the geometry wrong. When contact angle or exact ball diameter is unknown, two field rules of thumb get you close for ball bearings: BPFO ≈ 0.4 × N × n and BPFI ≈ 0.6 × N × n. They are approximations, not substitutes for the datasheet, but they will tell you whether a peak is plausibly a bearing tone.

How do you read a defect frequency in the spectrum?

A single peak at a calculated frequency is suggestive; the surrounding pattern is what confirms a bearing fault and grades its severity. Three features matter.

Bearing defect signature in a vibration spectrumAn outer-race defect signaturefrequency (Hz) →amplitude1x BPFO2x3x4x1x-RPM sidebandsevenly spaced harmonics = a repeating impact
The confirming pattern. A bearing defect rarely shows as one clean peak, look for a family of evenly spaced harmonics of the defect frequency, each flanked by running-speed sidebands.

How fast does a bearing defect grow once you see it?

A detected defect frequency is the start of a race against a clock, and knowing roughly how much time you have is what turns a scary spectrum into a calm plan. Reliability engineers describe this window with the P-F interval: the time from the point a defect first becomes detectable (P, potential failure) to the point of functional failure (F). For rolling bearings, ultrasonic and envelope methods catch the defect earliest, followed by vibration, then heat, then audible noise, and finally catastrophic failure, each stage leaving less runway than the last.

Practically, that means the value of defect-frequency analysis is proportional to how early in the P-F interval you catch it. An outer-race spall found by envelope analysis at stage one might give you weeks of planning time; the same fault first noticed because the bearing is now hot to the touch might give you days. This is the entire economic argument for routine vibration routes over run-to-failure: the earlier the P, the more of the interval you get to spend planning instead of scrambling. It is also why a single alarming reading should trigger a shortened re-measurement interval, not an immediate teardown, you want to see the trend so you can place the repair in the cheapest possible window rather than the first available one. This is the same logic that governs every condition-based maintenance trigger and keeps unplanned events off the maintenance backlog.

A 5-step workflow to diagnose a bearing from vibration data

  1. Get the exact bearing number and speed. Pull N, d, D, and θ from the manufacturer datasheet for that part number, and record actual running speed at the moment of measurement. Guessed geometry produces confident nonsense.
  2. Calculate all four frequencies. BPFO, BPFI, BSF, FTF in Hz. Verify the sanity check (BPFO + BPFI = N×n) before trusting the numbers.
  3. Match peaks to frequencies. Overlay the calculated frequencies on the measured spectrum. A match at a non-integer order that is not a known gear-mesh or blade-pass tone is your candidate fault.
  4. Confirm with harmonics and sidebands. Require a family of harmonics and speed-spaced sidebands, and use envelope analysis to catch it early. Grade severity by how many harmonics appear and how fast amplitude and sidebands are trending, not by a single reading.
  5. Trend, then plan the fix. One spectrum is a snapshot; the trend is the diagnosis. Rising BPFO energy over successive routes means schedule the change-out in a planned window and stage the parts, the whole point of catching it early. Feed the finding into your MTBF history and spare-parts plan.

What the standards and numbers say

Defect frequencies are only useful if someone is collecting and trending the data on a route, and if the finding actually reaches the planner. That is the connective-tissue problem Harmony is built for: it pulls machine signals, condition readings, and maintenance history into one operational data layer, surfaces the developing pattern, and can draft the work order for a human to approve, layered onto the CMMS and machines you already run, with no rip-and-replace. See how the platform works or the CLS case study for one plant's path to trustworthy floor data. For the bigger picture on where vibration analysis fits, start with the equipment reliability maturity ladder, and pair defect-frequency work with the bearing failure modes guide so the vibration tells you not just which surface but why it is failing.