With the heightened degree of technological advance in the recent years, many aspects of life have seen major changes – from our daily activities, to the way companies conduct their businesses including the production and distribution of goods. Digital transformation-enabling concepts like the Internet of Things (IoT) and advanced analytics have contributed significantly to the nature of machine maintenance, by assessing patterns of machine behavior that may be the cause of potential failures.
Machine failures are common and expensive problems for manufacturers, who have resorted to developing different strategic approaches in order to eliminate or limit them. Three of the most popular strategies when it comes to machine maintenance are reactive, preventative and predictive maintenance.
But what are those and what is the difference that distinguishes them from one another? In the following article we will be outlining the characteristics which separate the three types of maintenance, as well as downsides and possibilities which they hold.
To help understand the concepts better, we will begin by visualizing with a simpler example. Instead of industrial machines, let us say that the object which we are trying to repair is a smartphone. The principles of each of the three maintenance types could be described as follows.
“Waiting for the smartphone to break”
In this case we do not act on any potential technical difficulties until they become too troublesome or completely disrupt the usage of the device. We will not be wasting any effort beforehand, but when disruption occurs, we cannot use our device for some time, which may lead to great losses if our business heavily depends on the operation of the particular smartphone.
“Servicing the smartphone at regular intervals”
For this strategy we plan to repair or optimize performance of our device regularly over a specific timeframe (every year or every six months etc.). This, however, despite being a more reasonable practice, does not take into consideration other conditions that interfere with the performance of the smartphone. For example, even if maintenance for the standard smartphone is needed in a year, some people might use their device considerably more time, or use applications that drain battery life, leading to it wearing out faster than the standard. Another factor that this strategy does not take into consideration is that the device might break down in between the estimated times for maintenance, which presents the same issues as the reactive maintenance approach.
“Checking battery life and usage metrics constantly”
Following this strategy, we can keep track of key parameters affecting phone performance in real-time. This includes looking out for signs that might show inappropriate usage and changing app usage behavior to preserve battery life. Even downloading an application that does a specific type of monitoring to protect the smartphone from unwanted battery damage due to negligence can qualify as a predictive strategy.
When exploring these strategies form a manufacturer’s perspective, however, those differences are even more important, as the scale of their impact on businesses is much greater. We now proceed with an extended overview regarding the three maintenance types in an industrial context.
Reactive maintenance (also referred to as breakdown or corrective) is less cost effective at first glance, as it does not require great investments to begin with, but in the long run it has the potential to be costlier. As the strategy requires action only after the machine stops working, this may result in temporary halting of the production process and thus causing losses in time, money, and efficiencies.
Preventative maintenance is still a popular choice amongst most manufacturers, as it was a prevalent standard before the advent of the IoT and advanced analytics capabilities. Although it limits the possibilities of major failures and decrease the probability of machine downtime, this approach could result in more volatility of staffing in addition to excessive maintenance. Preventative maintenance does not take into consideration the differences in machine use and other factors affecting their lifespan resulting in equipment in good condition to be unnecessarily mended.
Predictive maintenance strategy involves monitoring and analyzing KPIs regarding machine operation in real-time. This is achieved by equipping the machines with sensors/ IoT devices which measure various critical parameters. Important insights like ‘probability of future failure’ and ‘top failure causing parameters’ are gained from the analyzed data. Cons include high initial investment and data volume, and complexity of deployment. On the other hand, this strategy has a potential to significantly reduce downtime, cut routine maintenance costs and increase asset lifespan.
One example is the usage of vibration sensors (highly applicable for large rotating machinery), which monitors each machine’s vibration and indicate when a problem is about to occur. Other practices include ultrasonic analysis, thermal imaging, measurement of pressure and shock, and so on with most of them conducted while machines are still working.
We hope that this article was useful to you! To read more on the topic of Predictive Maintenance you can refer our article titled ‘The Nature of Predictive Maintenance’, and ‘Predictive Maintenance, a revolutionary use case made powerful due to the advent of Internet of Things (IoT)’