Understanding the Shock Pulse Method (SPM)

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The Shock Pulse Method (SPM) is a specialised, proprietary condition monitoring technique developed primarily to assess the health of rolling-element bearings. It is a branch of vibration analysis, but its methodology departs sharply from conventional spectral analysis: instead of building a frequency spectrum, SPM measures the high-frequency shock waves a bearing emits each time a rolling element rolls over a flaw such as a spall or crack. A healthy, well-lubricated bearing produces a quiet, low-level shock-pulse pattern; a damaged bearing produces strong, distinct pulses that the instrument detects with ease.

1. Definition: What Is the Shock Pulse Method?

SPM rests on a simple physical fact. When two hard steel surfaces meet abruptly — a rolling element striking the edge of a pit, or a momentarily dry contact under load — the collision launches an ultrasonic pressure wave through the material. That pressure wave, the “shock pulse”, arrives before and apart from the slower mechanical vibration that follows it. By measuring the shock pulse directly rather than the bulk vibration of the housing, SPM gains an early, clean window into both the lubrication state and the surface condition of the bearing. Because the method is sensitive to the impact itself, it can detect a developing bearing defect long before that defect grows large enough to dominate a velocity spectrum.

2. How SPM Works

The heart of the technique is a purpose-built accelerometer paired with a tightly defined measurement procedure:

1. Tuned accelerometer: SPM uses a sensor deliberately tuned to resonate at a very high frequency — typically around 32 kHz. This mechanical resonance acts as an amplifier, making the sensor exquisitely sensitive to the high-frequency, low-energy impacts a bearing flaw produces, while ignoring ordinary low-frequency machine vibration.
2. Shock-pulse detection: The instrument captures the transient pressure waves from each impact. It is engineered to respond to the shock wave of the collision itself, not to the slower structural vibration that the impact subsequently sets off.
3. Signal processing: The raw signal is reduced to two key numbers:
   • Carpet value (dBc): the steady background level of weak shock pulses. It reflects the overall lubrication condition — a high carpet value points to a thin or failing oil film and the continuous, rough metal-to-metal rolling contact that results.
   • Maximum value (dBm): the strongest single pulse seen during the measurement. A high maximum value is a clear sign of a discrete physical defect such as a spall or crack.
4. Data normalisation: Crucially, the raw decibel readings are normalised against the bearing’s size (shaft diameter) and rotational speed. This correction lets the system collapse the result into a simple, colour-coded verdict — green, yellow, red — that a technician can read at a glance without specialist interpretation.

The gap between the carpet and maximum values is itself diagnostic: a low carpet with an occasional high maximum suggests an isolated defect, whereas a steadily rising carpet usually means the lubrication is breaking down. That separation of lubrication from damage is one reason SPM complements other condition monitoring methods so well.

3. SPM versus Envelope Analysis

SPM is conceptually close to envelope analysis (demodulation), another widely used way of catching bearing faults. Both techniques aim to pull the repetitive, low-energy impacts of a bearing defect out of the machine’s noisy background vibration, and both rely on the high-frequency stress waves a flaw generates. They differ in how they do it:

Both are highly effective. Envelope analysis usually delivers a finer diagnosis — its envelope spectrum can separate an inner-race fault from an outer-race one by matching peaks to the calculated bearing fault frequencies (BPFO, BPFI and the rest). SPM, by contrast, is prized for its simplicity, repeatability and its uncommon ability to flag lubrication problems before any physical damage has even begun.

4. Applications

SPM earns its place in a great many predictive maintenance programmes, and it is particularly strong in three areas:

• Early bearing fault detection: it picks up defects at a very early stage, giving planners ample lead time to source parts and schedule a replacement during a convenient outage.
• Condition-based lubrication: by watching the carpet value, technicians know when a bearing is starved of grease, and can confirm afterwards that re-greasing actually restored the oil film. This turns blind, calendar-based greasing into a measured, condition-based task.
• Slow-speed machinery: because it responds to impacts rather than to the energy of sustained vibration, SPM remains effective on very slow bearings — the kind that defeat conventional vibration analysis, where each defect produces only a handful of low-energy events per minute.

5. SPM in a Broader Diagnostic Toolkit

SPM is excellent at answering one question — “is this bearing healthy?” — but it does not address the other faults that plague rotating machinery, such as unbalance and misalignment. In practice it sits alongside broadband vibration measurement and field balancing. A portable two-channel analyser such as the Balanset-1A measures the 1× amplitude and phase needed to diagnose and correct unbalance in the machine’s own bearings, while a shock-pulse or enveloping check confirms those bearings are fit to keep running. Used together, the two views give a far more complete picture of machine health than either could alone — and they remind us that bearing condition should always be verified before a rotor is balanced, since balancing a machine with failing bearings only postpones the inevitable.

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