Advancements in semiconductor technologies and capabilities are enabling new opportunities to sense, measure, interpret, and analyze data in industrial applications and, in particular, condition-based monitoring solutions. Next-generation sensors based on MEMS technology, combined with advanced algorithms for diagnostic and prognostic applications, expand opportunities to measure a variety of machines and improve the ability to effectively monitor equipment, improve uptime, enhance process quality, and increase throughput.

To enable these new capabilities and capture the benefits of condition-based monitoring, new solutions must be accurate, reliable, and robust so that real-time monitoring can expand beyond basic detection of potential equipment faults to deliver insightful and actionable information. Performance of next-generation technologies combined with system-level insights enable a deeper understanding into the application and requirements necessary to solve these challenges.

Vibration, one of the key components of machine diagnostics, has been reliably used to monitor the most critical equipment across a wide range of industrial applications. A significant amount of literature exists to support the various diagnostic and predictive capabilities required to enable advanced vibration monitoring solutions. Less well covered is the relationship between vibration sensor performance parameters, such as bandwidth and noise density, and end application fault diagnostic capabilities. This article addresses the major machine fault types in industrial automation applications and identifies the key vibration sensor performance parameters that are relevant to the specific faults.

Several common fault types and their characteristics are highlighted below to provide insights into some of the key system requirements that must be considered when developing a condition-based monitoring solution. These include—but are not limited to—imbalance, misalignment, gear faults, and rolling bearing defects.

Imbalance

What is imbalance and what causes it?

Imbalance is an unequal distribution of mass that causes the load to shift the center of mass away from the center of rotation. System imbalances can be attributed to improper installations such as coupling eccentricity, system design errors, component faults, and even accumulation of debris or other contaminates. As an example, the cooling fans built into most induction motors can become unbalanced due to an uneven accumulation of dust and grease, or due to broken fan blades.

Why is an unbalanced system a concern?

Unbalanced systems create excess vibrations that mechanically couple to other components within the system such as bearings, couplings, and loads—potentially accelerating the deterioration of components that are in good operating condition.

How to detect and diagnose imbalance

Increases in overall system vibration can point to a potential fault created by an unbalanced system, but diagnosis of the root cause of the increased vibration is performed through analysis in the frequency domain. Unbalanced systems produce a signal at the rotational rate of the system—typically referred to as 1×—with a magnitude that is proportional to the square of the rotational rate, F = m × w2. The 1× component is typically always present in the frequency domain, so identification of an unbalanced system is done by measuring the magnitude of the 1× and the harmonics. If the magnitude of the 1× is higher than the baseline measurement and the harmonics are much less than the 1×, then an unbalanced system is likely. Both horizontally and vertically phase-shifted vibration components are also likely in an unbalanced system.1

What system specifications must be considered when diagnosing an unbalanced system?

Low noise is required to reduce the sensor influence and enable detection of small signals created by an unbalanced system. This is important for the sensor, signal conditioning, and acquisition platform.

Sufficient resolution of the acquisition system to extract the signal (especially the baseline signal) is required to detect these small imbalances.

Bandwidth is necessary to capture sufficient information beyond just the rotational rates to improve the accuracy and confidence of a diagnosis. The 1× harmonic can be influenced by other system faults, such as misalignment or mechanical looseness, so analysis of the harmonics of the rotation rate, or 1× frequency, can help differentiate from system noise and other potential faults.1 For slower rotating machines, fundamental rotation rates can be well below 10 rpm, meaning the low frequency response of the sensor is critical for capturing the fundamental rotation rates. Analog Devices’ MEMS sensor technology enables detection of signals down to dc and provides the ability to measure slower rotation equipment, while also enabling measurement of wide bandwidths for higher frequency content typically associated with bearing and gearbox defects.

Why is misalignment a concern?

Misalignment errors can impact the greater system by forcing components to operate under higher stresses, or loads, than what the components were originally designed to handle and can ultimately cause premature failures.

How to detect and diagnose misalignments  
Misalignment errors typically show up as the second harmonic of the rotational rate of the system, referred to as 2×. The 2× component is not always present in the frequency response, but when it is, the relationship of the magnitude to the 1× can be used to determine whether a misalignment is present. Increased misalignments can excite harmonics out to 10× depending on the type of misalignment, the location at which it is measured, and the directional information.1 Figure 4 highlights the signatures associated with potential misalignment failures.