Rise of the plug-and-play CM system19 January 2022

‘Plug and play’ condition monitoring offerings over the past year claim to do away with the need for expert analysis (at a certain low level, anyway), thanks to automation, cloud computing and standardised hardware. By Jody Muelaner

Vibration analysis is a tool commonly used for both early fault detection and troubleshooting of machinery. A skilled vibration analyst can identify and correct specific issues with machinery, including imbalance, bearing wear, lack of lubrication and worn gears. Condition monitoring is a more automated approach that continuously measures signals such as vibration to determine when a fault may be imminent. This can greatly reduce the risk of catastrophic failure and secondary damage, therefore also reducing costly down-time, repairs and safety incidents.

Typically, condition monitoring requires a skilled analyst to position sensors and set sensible limits on vibration levels. Once set up, the system can run without further intervention until an issue with the condition of machinery is detected.

Vibrations can be measured as a displacement, a velocity or an acceleration. Typically an accelerometer is attached to the surface of a machine, and either the directly-measured vibration signal is analysed. Many faults including imbalances, looseness and electrical faults produce a sinusoidal vibration. Some faults, such as a broken gear tooth or a defect in a bearing race, will produce a periodic impulse.

Other faults may produce harmonics. The frequency of a vibration source is often linked to shaft running speeds or electrical AC frequencies, enabling vibration levels to be associated with specific faults. For example, a vibration at 1x the shaft running speed could indicate imbalance or angular misalignment. Vibration at an integer multiple of shaft running speed might indicate parallel misalignment, loose bearings or damaged gears. Similarly, vibrations as integer multiples of the AC supply frequency can indicate distortion of AC motor windings or a faulty rectifier.

The direction in which a particular vibration frequency has the highest amplitude also depends on the specific fault. For example, angular misalignment between shafts drives vibration in the axial direction, while parallel misalignment drives vibrations in the radial direction. Thoughtful placement of sensors is therefore required to detect anticipated faults, and if necessary differentiate between them.

In real machines, there are many different sources of vibration, resulting in an apparently-chaotic signal made up of sinusoidal and impulse vibrations with many different underlying frequencies. Fast Fourier Transform (FFT) algorithms are used to separate out these underlying frequencies, producing a spectral plot showing the amplitude of vibration across a range of frequencies (pictured, right). Spikes on this plot indicate identifiable causes of vibration.

The set-up of a condition monitoring system would involve first measuring the baseline condition of a machine, identifying characteristic frequencies in a spectral plot. For machinery running at variable speeds, it may be necessary to monitor shaft speed so that the characteristic frequencies can be defined in terms of the shaft running speed rather than as an absolute frequency.


A number of condition monitoring systems are now available that seek to enable rapid setup without requiring an on-site expert. One established system is the Fluke Connect Asset Health Dashboard, which provides app access to sensor data. The SKF Plug and Play system (pictured, above) is intended for rotating equipment. It consists of the QuickCollect sensor, a vibration and temperature sensor, and the SKF Pulse app (pictured right) which is used to collect, store and share data using an Android or Apple device. SKF also provides remote advice.

Correct placement of sensors is the vital first step to obtaining valid vibration data. The SKF approach guides the user using the app interface. “Customers can easily create assets from within the Pulse app using predefined models for balance-of-plant machinery. Customers are intuitively guided to enter specific mandatory machine parameters, photographs, asset ID’s, nameplates, etc. The machine templates provide visual indication of where the user should place the sensor,” says Barrie Rodgers, SKF manager, market insights, legal and strategic communications, connected technologies.

After taking baseline measurements, alarm levels can be set according to 10813-2. This is the approach taken manually by experts, as well as the apps available from suppliers such as Fluke and SKF.

However, it can be dangerous to simply apply statistical rules to baseline data without further thought. According to Andy Mellor, MD at Pragmatic Maintenance & Reliability: “The problem is, most machines haven’t read the ISO standards… Unless you have some capability of learning within the system, you’re going to be on sticky ground.

ISO 10816-1 sets the tone for other casing vibration standards for machinery acceptance, even in that it has a clause talking about alarm levels and trip levels, which says that they’ve got to be set based on experience and review of absolute trend levels. So having a monitoring system that just has alarm levels in it based on a standard, doesn’t meet the standard.”

The SKF Pulse app also sets alarm levels that indicate when a machine requires further attention. It combines default alarm levels for velocity based on ISO 10813-2 with levels for acceleration “based on SKF’s patented algorithms for bearing defects (based on bearing bore diameter and running speed) and temperature.”

It would appear that the system simply tells you that vibration levels from a spectrum analysis indicate some type of fault. “From within the app, the alarm levels will indicate a deviation from the baseline, but no specific fault types are indicated,” says Rodgers. The customer can then request a more detailed diagnostic report directly from the app.

The types of fault condition that can be detected include bearing defects, imbalance and looseness. The database providing detailed diagnostics for bearings doesn’t only cover SKF bearings. The level of diagnostics within a particular machine depends on the information provided by the user setting up the system.

Rodgers adds: “This is not intended to provide a detailed root cause analysis, but recommended actions based on the data that is collected.” More sophisticated analysis with selectable frequency bands, resolutions and time-based signatures would require upgrading to a more advanced system.

Jody Muelaner

Related Companies
Fluke (UK) Ltd
Pragmatic Maintenance and Reliability Ltd
SKF (UK) Ltd

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