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Transforming a maintenance strategy with SaaS

10 May 2024

Fernando Velloso explains how a Software as a Service (SaaS) model can help manufacturers overcome any digital scepticism, and can also offer opportunities to improve total cost of ownership (TCO).

In 2017 Price Waterhouse Cooper (PwC) created a framework that identified four levels of maturity in predictive maintenance. As companies move through these levels, there is an increase in how much data they use to predict failures. Level four involves applying the power of machine learning techniques to identify meaningful patterns in data and to generate new, actionable insights for improving asset availability. PwC calls this Predictive Maintenance 4.0, or PdM 4.0, which offers the potential to predict failures that had have, traditionally been unpredictable.

PwC’s Predictive Maintenance 4.0: Predict the unpredictable report recommended three foundational building blocks for PdM 4.0. First, there is a need to establish a big data infrastructure to support the collection and processing of data from both internal and external sources. This includes considerations about in-house versus cloud-based storage solutions, with a focus on ensuring accessibility, speed, reliability and bandwidth of the communication network. 

Second, an Internet of Things (IoT) infrastructure is crucial in connecting assets wirelessly to the maintenance data center. This requires thoughtful decisions on wireless protocols, data encryption and security. Additionally, choosing an integrated data analytics platform is emphasised as being essential for efficient PdM 4.0 implementation, as existing enterprise resource planning (ERP) systems may lack the capabilities required for sophisticated data trends and analytic methods. 

Despite these recommendations, many food manufacturers remain hesitant about going digital. Research by Make UK, for example, found that while 45% of manufacturers have already introduced digital technologies, 15% had no plans to do so. So, how can these latecomers be encouraged to get aboard the Industry 4.0 bandwagon? The answer lies in using PdM 4.0 and big data to improve TCO.

Advanced sensors 
Some production managers are sceptical about whether new digital technologies can fit into their existing, and accepted, processes. This is understandable, as manufacturers traditionally been wary of any changes that could introduce risk to a process. So it is essential that Industry 4.0 integrates seamlessly into existing practices. 

This scepticism is also behind the reluctance to embrace SaaS models, with many being averse to monthly subscription fees when they are used to buying solutions up-front. 

One major driver for change is the use of data-driven insights gleaned from smart sensors. One food manufacturer, for example – a food and salad producer – decided to shift its plant maintenance from a manual system to a digitalised PdM 4.0 model similar to that recommended in PwC’s report. 

The manufacturer had previously relied on traditional maintenance processes – an individual maintenance specialist would circle the company’s plant and inspect the equipment. It wanted to improve on this process and to do so it chose WEG's Motor Fleet Management (MFM) platform. The MFM platform enables real-time monitoring of both vibration and temperature, providing insights into the health of machinery. MFM, which is offered as a monthly SaaS model, is compatible with the REST API application programming interface, so manufacturers can integrate data and reporting into its own preferred hardware or software. 

The salad producer was provided with three WEGscan sensors, which were attached to three of the WEG W22 motors on the factory floor. Data from the sensors could then be gathered into consolidated reports. The sensors are able to detect abnormal spikes in vibration in the motors, pre-empting a breakdown.

The success of this deployment led the manufacturer to deploy additional sensors on other critical assets. This brought the total up to five sensors, fortifying its digitalised PdM 4.0 strategy. Through this monitoring, the company was able to uncover the critical  and unexpected cause of the vibration spikes. The problem related to how machines were anchored to the floor.

The W22 motor was re-screwed to the plant floor to address the issue, a problem that might otherwise have gone unnoticed. Aside from gaining a more comprehensive understanding of asset health, the new PdM 4.0 approach allowed the company to strategically plan maintenance routes and allocate resources more efficiently. 

Uninterrupted flow 
The transition from traditional, time-based maintenance to a data-driven, condition-based approach has yielded substantial TCO benefits, not only in terms of cost savings but also helped ensure the uninterrupted flow of operations.

Crucial to this were MFM’s high-frequency vibration analysis capabilities. High-frequency analysis is more effective in predicting plant equipment failures because it captures data at shorter intervals, offering finer insights into machine behaviour. This enables early detection of anomalies or degradation, while, in contrast, low-frequency analysis, with longer intervals between data points, may miss critical warning signs. Manual methods – which often rely on periodic checks – are also limited by their lower frequency and may lead to delayed responses or potential failures.

High-frequency vibration analysis is especially useful for ensuring the detection of early-stage anomalies in motors, such as bearing wear or misalignments, which are indicative of impending failures. 

PwC’s Predictive Maintenance 4.0 report underscores the importance of the organisational support structure in PdM 4.0 implementation. By identifying potential equipment failures in their nascent stages, maintenance teams can take proactive measures to rectify them, averting costly and disruptive breakdowns. This proactive approach translates to lower TCO for equipment, as it minimises the need for costly emergency repairs and reduces downtime. 

Fernando Velloso is an application engineer – digital solutions at WEG.


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