Condition Monitoring is the measuring of specific equipment parameters, noting signs of any significant changes that could be indicative of an impending failure.
Condition monitoring is defined as the measuring of specific equipment parameters, such as vibrations in a machine, its temperature or the condition of its oil, taking note of any significant changes that could be indicative of an impending failure. Continuously monitoring the condition of equipment and taking note of any irregularities that would normally shorten an asset's lifespan allows maintenance or other preventive actions to be scheduled to address the issue(s) before they develop into more serious failures.
Condition monitoring is a big component of predictive maintenance. The data collected from condition monitoring over time provides valuable information about the current and historical state of an asset. This evolution of a machine can be used to anticipate how the asset will perform over time and how it might degrade, allowing for the scheduling of maintenance based on these predictions. This is known as predictive maintenance – maintenance based on what failures may occur and what maintenance should be scheduled to prevent such failures from occurring.
Condition monitoring techniques are typically used on rotating equipment (gearboxes, reciprocating machines, centrifugal machines, etc.), backup or secondary systems, and other machinery such as compressors, pumps, electric motors, presses and internal combustion engines.
There are two common methods used for condition monitoring:
Condition monitoring via these two methods provides an inside look at how your machines and/or components are currently operating and, over time, offers a historical account of machine health.
Unsurprisingly, condition monitoring can lend itself to many benefits, including decreased maintenance costs, reduced downtime, extended asset life and cost savings on prematurely changed resources. For example, your condition monitoring system measures the amount of noise produced by a component. Over time, you notice a trend of machine failure soon after the noise level on the component reaches a certain level. Because you have a condition monitoring system in place, you can now set an alert on that component when it hits that noise level, which, in turn, lets maintenance personnel know they might want to consider replacing the component.
Modern technology has taken condition monitoring online (as will be discussed later), so internet-enabled and wireless-connected sensors and software provide real-time measurements around the clock. These measurements are relayed to integrated software for analysis and the storing of historical datasets.
Condition monitoring tools that spit out data in real time enable you to determine the root cause of an issue quicker, and wireless sensors on assets automatically connect employees with real-time data via remote access using smartphones or tablets.
Condition monitoring lets your facility go from a reactive approach to more of a predictive maintenance program. Once in place, condition monitoring provides you with 24/7 measurements, showcasing a clear picture of the health of your machines without adding additional labor.
Condition monitoring systems rely on visual data gathered from multiple sensors integrated with a software system. This means an added cost of purchasing and installing these sensors, as well as purchasing the tools necessary for condition monitoring (vibration analysis, infrared thermographers, etc.). There's also the added cost of training employees to use condition monitoring technology accurately and effectively.
Additionally, condition monitoring sensors might have trouble working properly under especially harsh operating conditions. Such conditions can damage sensors, forcing you to replace them on a more regular basis than anticipated.
Condition monitoring techniques are standardized through ISO and the American Society for Testing and Materials (ASTM). ASTM outlines a variety of standards, mostly dealing with condition monitoring for in-service lubricants, while ISO standards 13372, 18436, 17359 and 13381 (among others) specify the guidelines for condition monitoring and diagnostics of machines.
Below are the most common techniques used for gathering data on the current condition of machinery.
Ultrasound typically is applied in the shock pulse method (SPM) for condition monitoring – a technique using signals coming off rotating bearings as the baseline for efficient monitoring of machines. For example, imagine a metal ball hitting a metal bar. When the ball contacts the bar, a pressure wave spreads through both materials. The pressure wave is quickly damped out (transient). When the front of the pressure wave hits the shock pulse transducer, it causes a damped-out, back-and-forth movement of the transducer's mass. When the oil film on a bearing is thick, the shock pulse level is low (showing low peaks); when the level increases, the oil film thickness is reduced.
There are a variety of machine condition monitoring types and techniques, each serving a different role for collecting data. Below are the most common types.
Once the appropriate sensors are mounted on the asset in the correct spot, they can be wirelessly connected to a remote condition monitoring system where they will display real-time data. Most systems can integrate multiple types of sensor data (vibration, thermography, acoustics, etc.), so at any given time, you can get a snapshot of your asset's current condition. Online condition monitoring also lets you set up real-time alerts for remote devices or email.
You may be familiar with the term "portable machine diagnostics," where portable equipment is used to read data from mounted sensors. This is another way to describe a type of online condition monitoring.
Consider this scenario: You take your car in for its regularly scheduled maintenance. Two weeks later, it breaks down due to a completely different issue. Just like cars, machines are vulnerable to these random, unpredictable failures. Certain types of maintenance, like reliability-centered maintenance and predictive maintenance, are based on the principle that failure isn't always linear and requires analysis of several asset aspects to detect possible failure indications. This is why condition monitoring is so useful: it lets you monitor multiple facets at once using the techniques discussed above.
The P-F curve is a graph showing an asset's health over time to determine the interval between potential failure (P) and functional failure (F). Potential failure is defined as the initial point at which an asset starts deteriorating or failing. For instance, a history of recorded bearing failures could tell you that the bearing typically fails after its temperature exceeds 70 degrees. Functional failure is the point at which an asset has reached the limit of its usefulness and is no longer operational. For example, you have around five days from when the bearing temperature surpasses 70 degrees to when it fails. The P-F curve is set on an x-axis to measure time and a y-axis to quantify the asset's condition. In this example, you should be inspecting the bearing every two to three days.
Condition monitoring plays a significant role in detecting the P-F interval of the P-F curve. The P-F interval is the time between an asset's potential failure and its functional predicted failure. The idea is that your inspection interval should be smaller than the P-F curve to catch a failure before it occurs. Using condition monitoring lets you gauge an asset's condition, maximizing the P-F interval. Monitoring things like oil sampling and analysis, acoustic emission analysis, vibration analysis and infrared thermography are all condition monitoring-based techniques to give you an inside look at a machine's current condition.
The method and frequency of monitoring make a difference in the length of the P-F interval, according to Dale Blann, CEO of Marshall Institute. Blann says technology-based online condition monitoring provides the greatest P-F intervals and is less disruptive than other types of inspections like offline inspections where machines are generally shut down.
The industrial internet of things (IIoT) is essentially a network of interrelated devices on mechanical and digital machines that give you the ability to transfer data over a large network without needing human-to-human or human-to-computer interaction. Modern condition monitoring systems use the IIoT to integrate numerous types of monitoring software into one system in real time, from anywhere in the world and across multiple devices.
IIoT-connected condition monitoring systems enable organizations to easily monitor several facets of each asset and identify performance, detect issues and even automatically schedule maintenance based on preset limits. Some of the biggest advantages of IIoT-connected condition monitoring include:
When building an IIoT-connected condition monitoring system, a few things should be considered before sensors and other equipment are purchased. It's important to take into account the type of equipment you will be monitoring, the data variables (what information you want to collect) and how you'll use the data.
How often do you plan to review the data? Generally, the more frequently you need to review data, the more bandwidth/data storage is required. You can also purchase a system that allows you to set predetermined times for when data is reviewed. For example, maybe you only want to check a certain asset at the beginning of a shift and review the data twice a day but still receive alerts when the data exceeds the preset limits.
Do you have an internet connection and power capabilities in the area near your equipment? If not, that is an extra cost you'll need to factor into the overall budget.
Is your equipment indoors or outdoors? Outdoor environments can limit the ability to get an internet connection wherever the equipment is located. Additionally, outdoor settings inflict harsher conditions on sensors and other condition monitoring equipment, so you may need to consider weatherproof or more durable sensors.