Low-cost vibration sensor with low frequency resonance for condition monitoring of low speed bearings: a feasibility study

Condition monitoring (CM) of rolling element bearings (REBs) rotating at low speeds poses some challenges in practice because of the low signal-to-noise ratio produced by the faults. Both vibration-based and ultrasound/acoustic emission (AE) based sensing techniques have been proposed in the literature to detect faults in rolling element bearings running at low speeds. The vibration-based technique generally works within the frequency band from 0 to 20 kHz. Meanwhile, the ultrasound/AE-based techniques work in a very high-frequency band, from 20 kHz up to 1 MHz. Consequently, processing ultrasound/AE sensor data requires more computational resources compared to processing vibration data. Moreover, the hardware investment to build an ultrasound/AE-based CM system is more expensive than that of a vibration-based CM system. Since hardware and software cost is one of the main bottlenecks of the adoption of CM systems in the industry, it is, therefore, necessary to develop a cost-effective vibration-based CM system for critical bearings mounted on low-rotational speed machines. The paper presents a feasibility study in evaluating the performance of an off-the-shelf low-cost vibration sensor (10 – 20 times cheaper than high-end vibration sensors) with a low resonance frequency (around 75 Hz) to diagnose faults on REBs operating at a low rotational speed of 30 - 60 rpm. The low-resonance frequency characteristic of the low-cost sensor allows us to acquire the data at a low sampling rate of 400 Hz. A theoretical justification of why a vibration sensor with low-resonance frequency can still be effective for low-speed bearing fault diagnosis is given. This feasibility was experimentally validated on a test rig on which an REB with a seeded fault on the outer race was tested as a case study. A high-end vibration sensor (accelerometer) acquired at a 20 kHz sampling rate was also used as a benchmark. The vibration signals measured by the low-cost and high-end sensors in four different operating conditions were analysed with the well-established envelope analysis. In addition, the high-end sensor signals were also analysed with the state-of-the-art Spectral Correlation (SC) technique to compute the Enhanced Envelope Spectrum (EES) for bearing fault detection and diagnosis. The results confirmed that the bearing fault could be successfully detected and diagnosed in all the test conditions by the low-cost sensor analysed with the envelope analysis technique. On the other hand, the high-end sensor analysed with the SC technique could only diagnose the bearing fault for the least challenging test condition. The outstanding diagnostic capability of the low-cost sensor sampled at a low sampling rate has set a milestone that would enable the future development of a low-cost CM system for low-speed bearing applications.