{"id":43617,"date":"2026-05-16T12:46:05","date_gmt":"2026-05-16T07:16:05","guid":{"rendered":"https:\/\/www.verdantis.com\/?p=43617"},"modified":"2026-06-03T15:05:51","modified_gmt":"2026-06-03T09:35:51","slug":"condition-monitoring","status":"publish","type":"post","link":"https:\/\/www.verdantis.com\/de\/condition-monitoring\/","title":{"rendered":"Condition Monitoring for Plant Maintenance and Asset Reliability"},"content":{"rendered":"
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A production line in a large manufacturing facility comes to a sudden halt. It\u2019s a critical pump in the cooling system that fails without warning. As the production belt starts to heat up, operators are forced to shut down the entire process. What follows is a chain reaction, starting with idle labor and missed delivery timelines, culminating in expedited spare parts and mounting pressure.<\/span><\/p>

Do you know that such unplanned downtime costs US manufacturers up to <\/span>$207 million per week<\/span><\/a>? And as many as 55% of businesses face these scenarios once every year.<\/span><\/p>

What if this downtime can be planned for? Many asset-heavy industries already have planned downtimes in their strategies. They are usually for planned maintenance when assets are not in use or similar. However, organizations can also prevent unplanned downtime by mitigating unexpected events. And that\u2019s where condition monitoring comes into the picture.<\/span><\/p>

If you are wondering what condition monitoring is, it is the process of continuously or periodically tracking the health of machinery or equipment. This is done with the help of sensors that capture data on an asset\u2019s health factors, such as vibration, oil, temperature, and pressure. Based on this data, maintenance teams can detect anomalies from normal operating behavior.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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How Condition-Based Monitoring Shifts Maintenance Thinking<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Equipment is never predictable, and yet, most traditional strategies were built around predictability. Teams relied on fixed schedules based on the predictability of failures. For example, a machine that is more prone to failure would receive maintenance twice a month. Something like a bearing, on the other hand, will be replaced every six months.<\/span><\/p>

While this approach offers a structure, it also introduces inefficiencies. What if a component of a system is replaced earlier than its life expectancy? Or what if a component fails because its health was never assessed in real-time?<\/span><\/p>

Condition monitoring changes this from the foundational level. Put simply, it shifts the entire focus from when maintenance should happen to why it should happen.<\/span><\/p>

This shift introduces a benchmark operational expectancy from a machine or an asset. Over time, sensors help track deviations from that benchmark, enabling maintenance teams to be proactive. Instead of waiting for failure to occur and then reacting, maintenance can begin as issues start to appear.<\/span><\/p>

Because of this shift, the demand for condition monitoring systems is rising globally. According to <\/span>Fortune Business Insights<\/span><\/a>, the market was worth $3 billion in 2025. With a CAGR of 9.7% from 2026 to 2034, it is estimated to more than double and reach $6.8 billion.<\/span><\/p>

This shift is not just about transitioning from calendar-based to condition-based maintenance. It is also about switching from lagging to leading indicators and from reactive maintenance to proactive planning.<\/span><\/p>

The table below shows how condition-based monitoring shifts the entire maintenance organization:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Merkmal<\/b><\/th>\nTraditional monitoring<\/th>\nCondition-based monitoring<\/th>\n<\/tr>\n<\/thead>\n
Primary trigger<\/td>\nThis monitoring is based on fixed schedules, so inspection during one of those cycles or complete failure will be the trigger point<\/td>\nTriggers are based on real-time thresholds<\/td>\n<\/tr>\n
Asset knowledge<\/td>\nMaintenance plans are curated based on general manufacturer recommendations<\/td>\nKnowledge of the vital signs of each asset is gathered in real time<\/td>\n<\/tr>\n
Team culture<\/td>\nTeams are rewarded for fixing things fast<\/td>\nHere, the reward is not for fixing but for keeping things running<\/td>\n<\/tr>\n
Inventory<\/td>\nStocked based on historical buying patterns<\/td>\nInventory is optimized at the right time based on asset health and failure prediction<\/td>\n<\/tr>\n\n<\/tbody>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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A Multi-Parameter View of Asset Monitoring<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Although it is possible for equipment to fail for a single reason, that is not the case more often than not. Mechanical, thermal, and chemical changes can all occur simultaneously. That\u2019s why it becomes important to consider multiple aspects of an asset’s health at once. Asset condition monitoring facilitates this by tracking different health parameters.<\/span><\/p>

Each of these parameters gives a glimpse into how a machine is operating in real time. Temperature, for instance, highlights thermal stress and energy loss, and is one of the earliest signs of fault. It points to increased friction in bearings, electrical resistance in motors, or reduced cooling efficiency.<\/span><\/p>

Pressure, on the other hand, provides insight into fluid flow. For example, a drop in pressure indicates a leak somewhere, while a spike could mean resistance or a blockage.<\/span><\/p>

Oil quality parameters also serve as diagnostic media for direct evidence from inside the machine. Viscosity influences lubrication performance, metal particles signal wear, and water contamination accelerates corrosion. Since every imbalance, misalignment, looseness, and bearing defect generates unique frequency patterns, vibration becomes another information-rich factor.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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All these parameters can provide insights into machine failure at an early stage. However, it is best to interpret these signals together. This will get you a holistic view of the problem. Through this view, you can work with the maintenance and engineering teams to identify and address the issues at their root causes.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Condition Monitoring Techniques<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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As noted in the previous section, there are multiple equipment parameters you must monitor. And for that, different techniques are used, each leveraging a different type of sensor to collect relevant information. Understanding which technique is the best fit to find what anomalies makes the entire condition-based monitoring a whole lot easier.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Vibration Monitoring<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Analyzing vibrations of rotating machinery is one of the oldest and most used condition monitoring techniques of condition-based monitoring. According to <\/span>Future Market Insights<\/span><\/a>, it holds around 30.6% of the total condition monitoring system market share.<\/span><\/p>

The sensors used for this monitoring technique are called accelerometers. They collect vibration data that you can process using frequency spectrum analysis as well as envelope detection.<\/span><\/p>

The primary goal of these systems is to detect imbalance, shaft misalignment, and other mechanical issues at early stages.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Electromagnetic Monitoring<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Electromagnetic monitoring tracks magnetic field variations, which is why it works only for electrical equipment. The analysis output from this technique is associated with the flow of current in a motor, or with how the rotor interacts with the stator.<\/span><\/p>

Sensors used here collect flux-pattern data and electromagnetic emissions from a running machine. These signals can then be analyzed for asymmetry or distortion.<\/span><\/p>

Electromagnetic monitoring is best at detection:<\/span><\/p>