Predictive Maintenance: Diagnosing Performance and Capability

Predictive Maintenance

Sensors are the crucial guide and stimulant in mechanism

Many industrial motors are designed to work up to 20 years in continuous production applications such as chemical and food processing plants and power generation facilities, but some motors do not reach their projected lifetime. This could be due to insufficient operation of the motor, insufficient maintenance programs or lack of investment in Predictive Maintenance systems, or not having a Predictive Maintenance system in place at all which enables maintenance teams to schedule repairs and avoid unplanned downtime. Early prediction of machine faults through Predictive Maintenance can also help maintenance engineers identify and repair motors running inefficiently, enabling increased performance, productivity, asset availability, and lifetime.

During the early days, machines were not too complex and hence there were limited breakdowns. However, with the advancements in machines through program and logic controllers, the scenario of fewer breakdowns has changed. Previously, there was more of maintenance through manual labour on contrary to what it is now. The manufacturing units and factories want to stay competitive by rapid assembly line and tremendous automation through complex machines. This assist in measuring performance metrics such as production efficiency, output, and equipment efficiency. Because of all this, the maintenance, which was done only during equipment failure has now become a routine scheduled activity known as Preventive Maintenance.

Predictive maintenance involves a combination of techniques such as Condition-based monitoring (CbM), machine learning, and analytics to predict upcoming machine or asset failures. When monitoring the health of a machine, it is critically important to select the most suitable sensors to ensure faults can be detected, diagnosed, and even predicted. There are many sensors currently used to sense and detect faults, in rotating machinery and their loads, with the end goal of avoiding unplanned downtime. Ranking each sensor is difficult as Predictive maintenance techniques are applied to a multitude of rotating machines and nonrotating machines.

Some sensors can detect certain faults, such as bearing damage, much earlier than others. The sensors most commonly used to detect faults at the earliest possible moment are discussed, namely accelerometers and microphones. Most PdM systems will only employ some of these sensors, so it is imperative to ensure potential critical faults are well understood along with the sensors that are best suited to detecting them. The key to successful implementation of a predictive maintenance program is utilizing the proper sensors to determine machine condition. Sensor selection starts with an understanding of a machine’s potential failure modes and the warning signs associated with these modes. Typical warning signs on equipment with rotating parts include unbalance, bearing damage, cavitation (pumps), increased machine vibration levels, increased temperature of machine components, loss or reduction of lubrication flow, and loss or reduction of cooling water flow.

IoT and Machine Learning Technologies help the process improve much better

The advent of Internet of Things is changing the way of predictive maintenance. One can now have real-time monitoring devices at a low cost that send data to an algorithm on a continuous basis. It can detect whether there’s something going wrong with a machine or use machine learning to make a prediction. Predictive maintenance was often based on predictive analytics that required expert data scientists and complex machine learning models. This meant that machine learning was limited to a few large organizations. It wasn’t something you could do at scale. Even todays large organizations have the ability to continuously parse data through machine learning models and continuously get predictions back.

Economically the new low costs mean that it’s not just reserved for large organizations anymore. It’s also available to small and medium-sized businesses. It’s available to OEM’s and equipment manufacturers that want to provide predictive maintenance services with the equipment they are selling. The Internet of Things is having a profound effect on the manufacturing sector, leading to increased automation, more efficient operations, and the creation of valuable new business models. While the application of digital technologies can bring benefits across the value-chain, it is arguably in the area of predictive maintenance that the most significant impact can be derived.

Thus, Predictive maintenance for industry 4.0 is a method of preventing assets failure by monitoring production data to recognize patterns and estimate concerns before they happen. Previously factory managers and system operators carried out scheduled maintenance, other processes and regularly repaired machine parts to prevent downtime. It has rapidly emerged as a leading Industry 4.0 use case for manufacturers, factory managers and asset managers. By implementing IIOT technology to analyze asset nature, optimize maintenance schedules and gaining real-time alerts to operational risks, enables manufacturers to reduce service costs, enhance uptime and improve production throughput.

Manufacturers have been performing a time-based approach to the equipment maintenance. They used to consider the machinery age as important aspect for planning the maintenance routine. The older the equipment the more repeated maintenance procedures require to be carried out. As per stats some equipment has failed due to machinery age, while other failures occur randomly. It proves that a time-based approach is not that much cost-efficient. To avoid ineffective maintenance routine and costs that accompany it, manufacturers takes benefit of Industrial IoT predictive maintenance and data science. Based on readings from a machine, production efficiency can be improved either by increasing the time when machines are running through predictive maintenance or by predicting the number of goods that will pass or fail in quality inspection. That enables manufactures to reduce maintenance costs, expand equipment life, decrease downtime and enhance production quality by addressing problems before they cause equipment failures.

Energy Resources and Power Management

Power utilities, with their heterogeneous assets, have to deal with the crucial task of monitoring and maintaining their assets, while functioning with increased efficiency and reliability levels. Through the use of predictive maintenance technologies, power utilities can detect underperforming assets and enable the operating staff or personnel to understand the factors leading to abnormal operations, and accordingly schedule maintenance activities.

Maintaining an uninterrupted power supply is one of the top priorities for the energy sector. In particular, automated fault prediction addresses this challenge by employing Industrial Internet of Things (IIoT) sensors and implementing Machine Learning (ML) algorithms to continuously monitor a site. This ensures the collection of power usage data to build an early warning system to mitigate electricity downtimes, outages, and breakdowns. AI could help oil and gas companies predict when their machines and equipment require maintenance. Oil and gas companies can then repair these machines before their breakdowns result in long downtimes or employee injuries that could cost millions in legal fees and settlements.

With an interface on a computer or a smartphone, remote sensors can provide alerts in real time, potentially predicting issues in advance of a problem. Employees can proactively order parts, schedule the necessary maintenance outage and document actual conditions to create robust equipment history data. Over time, AI can monitor and look for patterns and trends in the data to better predict and potentially optimize future maintenance and performance activities. As wind and solar energy continue to shrink the gap for both price and performance with conventional energy sources, smart tech has the potential to be a powerful competitive advantage. An automated drone fleet can reduce the inspection time of an offshore wind farm. Remote sensing, powered by the Industrial Internet of Things (IIoT), can trigger predictive maintenance activities to extend the life expectancy of gear boxes, bearings and other equipment.

Manufacturing and Aviation Sectors are the crucial dependent

Manufacturing and Construction

The advent of digital manufacturing, sometimes referred to as Industry 4.0 introduces what has been called the ‘smart factory,’ in which cyber-physical systems monitor the physical processes of the factory and make decentralized decisions. Not only does this make for better products and enhanced productivity, but it also heralds a new era of predictive maintenance. Predictive maintenance solutions to support digital optimization represent the top investment category, ahead of investment in IoT and mobile app development. Respondents were almost evenly split among IT and operations-related roles within the manufacturing, CPG, and transportation/fleet industries. Predictive maintenance has the potential to add significant value to production processes by increasing efficiency and reducing unplanned and redundant costs. Additionally, the capability for better analysis of IIoT data makes IIoT devices more valuable, as more and more uses for the data are discovered.

Predictive maintenance focuses on the continuous monitoring of a machine’s health under normal working conditions without process interruptions to detect subtle changes that generally aren’t detectable during typical inspection processes. These subtle warning signs then become the triggers that indicate an impending problem, enabling operators and maintenance providers to plan for repairs and downtime, order necessary parts, and take other preparatory action to minimize the eventual disruption in processes.


Within aviation maintenance and engineering the aim of predictive maintenance is first to predict when a component failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs, thus reduce unscheduled removals and avoid Aircraft-on-Ground (AOG). One of the main challenges in aviation is to reduce costs and delays, while maintaining or improving current safety levels. A large percent of these costly delays are a result of unplanned maintenance such as when an aircraft has an abnormal behavior on the previous flight, creating an operational disruption and even requiring an aircraft change. Improving reliability and predicting failure are key aspects for reducing maintenance costs. Considering this, this is a subject undergoing intense study and large companies in the sector such as Airbus or Boeing are investigating it. Predictive aviation uses a software program that uses sensors and Flight Data Recorder (FDR) information to show if a failure may occur. The in-flight data is downloaded from the aircraft’s Flight Data Recorder to computer software where irregularities are identified. If any are detected, information is sent to schedule a check. Consequently, planes are less likely to stop working, have delays or incur cost.

By: Mannu Mathew | Sub Editor | ELE Times

Mannu Mathew
An engineer and a journalist, working, researching, and analyzing about the technology sphere from all possible vector, Currently working as a Sub-editor / Technology Correspondent at ELE Times