Predictive Maintenance, commonly abbreviated as PdM, uses sensors to gain real-time data and predict when an asset will require maintenance. This allows managers to take preventive measures to ensure that the equipment will not fail and that production processes will not suddenly halt. Hence, PdM helps to maximise uptime and enhance productivity by preventing unplanned breakdowns. It, therefore, is a reliable technology that managers utilise to reduce operational costs, time and labour whilst lengthening the lifespan of machinery. In fact, as per an article by McKinsey & Company, PdM technologies reduce downtime by 50 per cent, whereas asset lifespans are increased by 40 per cent.
People often confuse predictive maintenance with being the same as preventive maintenance. Although both forms of maintenance proactively detect problems to help reduce downtime, they are also distinct. The main difference in preventative maintenance is that it warns managers in different industries by relying on past data and industry guidelines. In contrast, predictive maintenance solely feeds on the Internet of Things (IoT) sensors and machine learning technology and engages in condition monitoring.
Predictive Maintenance in Manufacturing
Integrating predictive maintenance technology in manufacturing companies is an asset. As per the report by Markets Research Future, the global market for predictive maintenance is estimated to rise to USD 6.3 billion within two years. According to an article by McKinsey Digital, PdM in the manufacturing industry is estimated to save up to USD 630 billion by 2025. Experts have also noted that predictive maintenance can increase your ROI up to ten times.
Types of Predictive Maintenance in 2022
While the importance of predictive maintenance is no secret, choosing the right type may be challenging. This article will therefore explore the types of predictive maintenance manufacturers can choose. There are namely four:
Vibrational Analysis:
A type of predictive maintenance that monitors the vibration of a machine, the vibrational analysis will find issues in the machinery when vibrations change. It takes some time to ensure that this tool functions seamlessly, as it requires the standard rate of vibrations in each machine to first be recorded. It is only when the standard rate of vibrations starts to deviate that the system issues a warning notice. Sensors are fixed to places such as valves and motors of machines to monitor the vibrations, thereby noting the slightest malfunction. Specifically used for high rotating machinery, vibrational analysis can detect issues such as components in the machinery being loose, as well as when there is an imbalance and misalignment.
Sonic Acoustical Analysis:
Sonic Acoustical Analysis focuses on sounds that detect anomalies and is at times referred to as lubrication analysis. It acts as the stethoscope that monitors the heart of each machinery. With the number of devices in operation at the factory, it is generally hard to hear certain sounds. Implementing acoustical analysis will mean that sensors are placed near it to ensure that malfunctions can be detected through sounds that are audible to humans. Hence it measures the sound waves caused by components inside the equipment. This cost-effective solution is specifically used to detect low and high-rotating machinery. The most significant advantage of this type of predictive maintenance is that it reduces the amount of labour concerning relubrication. Through this, technicians can efficiently fix issues by addressing the root cause of the sound.
Ultrasonic Acoustical Analysis:
While sonic analysis can detect issues when there is only one moving sound coming from a machine, it is hard to track sounds when it is made of multiple moving parts. This is where ultrasonic acoustical analysis is applied. It is also applicable to situations where the factory has a larger number of assets and where the noise of the factory is too much. Any issue that is not within the human hearing range that causes stress or friction can be easily detected through this technology. With the help of Machine Learning, this technology will be trained to detect specific sounds that indicate failure. Thus, a work order is instantly sent to the manufacturer, who can address the issue before it escalates. Some regard ultrasonic acoustical analytics as a more robust form of PdM technology that foresees imminent breakdowns than other types.
Infrared Analysis:
Infrared Analysis focuses on the heat emitted by industrial machinery. It predicts issues where the temperature of the machine increases and, thus, is used to identify problems related to cooling, airflow, and sometimes motor stress. An additional benefit is that infrared analysis can detect moisture infiltration early. Usually, moisture can gather even when there is no water on the factory floor, so managers rarely consider this daily. However, when moisture does accumulate and prompt action is delayed, the entire machinery will be broken. This also safeguards employees from attempting to touch overheated machinery and injuring themselves. The system will, therefore, simply identify the wire causing the heat to rise and fix it. This is also a great method to reduce emissions being produced as the energy that is leaked or dissipated can be easily tracked.
Cerexio Predictive Maintenance: Preventing All Machine Anomalies
The Cerexio Predictive Maintenance solution is an intelligent infrastructure asset solution perfect for implementation in manufacturing factories. It is powered by Industry 4.0 technology, including Artificial Intelligence, Machine Learning models, and Predictive Maintenance technologies. The PdM is trained with past data records and realistic operational events to ensure that future-proof predictions are provided as far as ten years ahead. This is done by processing data from an integrated pool of IIoT systems, ERP, SCADA systems, sensors, databases and any other technologies present. With Cerexio Predictive Maintenance, you can therefore reduce your overall maintenance cost and rely more on your assets in the factory. It predicts failures, helps your finance team stick to their annual budget, and prescribes recommendations in a web-based system.
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Take Charge of the Health of Your Assets
The success of the manufacturing industry vastly depends on its machinery. As almost every function in the production process is now automated, manufacturers rely on their machines to complete a specific number of operations daily. This means whenever a machine breaks down, the number of daily targets is decreased. In other words, your work in the factory is haltered, and significant expenses will have to be incurred to either repair or replace them. Due to each machine’s important role, a technological solution that provides advanced warning would be an asset. Predictive Maintenance helps manufacturers to detect an issue well in advance and fix it before it escalates. Where maintenance is concerned, predictive maintenance will also provide you with details of the exact date that your assets should be checked. Are you going to take charge of your assets or hope for the best?
