Technical and operational feasibility of technological advancements in PdM

While assessing the technological advancements in predictive maintenance, it appears vital to study their technical and operational feasibility. The IIoT applications have become widely employed lately due to their significant advantages to the quality of operations and performance of related machinery. In fact, a large number of companies utilize different IIoT approaches for predictive maintenance as a way to decrease the maintenance costs and limit the frequency of maintenance tasks. The IoT platforms constitute a very good aid for

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predictive maintenance systems as they can integrate data from various machines or manufacturing equipment. However, an essential negative point in integrating industrial systems with IIoT platforms that must be indicated is the communication framework giving that the major communication protocols are usually incompatible and inconsistent with the modern communication protocols implemented in IIoT systems. This signifies that in order for the company to ensure a total technical feasibility of the IIoT platforms, it has to work harder on making the novel adopted communication tools and procedures match in a broader perspective with the existing ones. In other terms, the related organization ought to assure more solid and effective connections between the standard industrial devices and the web platforms. Therefore, the feasibility level depends to a large extent on the capabilities of the company to construct useful approaches and tools to translate the industrial machinery language in question to IIoT ready instruments and programs. In addition to that, the company must grant a special attention to the interoperability aspect which is mostly dependent on the standardization conditions and the communication protocols and represents the ability of the distinct IT systems and software to communicate, exchange and utilize information. (Lacob and Parpala, 2017).

It is quite obvious today and for most companies that combining predictive maintenance with artificial intelligence solutions can actually offer businesses the needed edge when it comes to maintaining their existing infrastructure or hardware. The major advantage of this blending is the warning that the company gets that a certain deficiency is about to be developed. If we also add to this, its ability to predict optimal operational patterns of the machinery, then we could easily conclude that the novel PdM solutions are much more efficient that the PdM traditional approaches. Still, a proper application of the relevant intelligent systems necessitates a convenient organizational work to garner entirely the expected rewards. In other words, the involved personnel, mainly the engineering maintenance teams, should be aware of the required advanced analytics competencies to build smart predictive maintenance systems.

For instance, arduous-to-measure enhancement systems or programs such as Six Sigma or Lean require to be controlled with the company’s new analytics abilities. Furthermore, maintenance workers must learn to employ the complete analytical capabilities of the presented intelligent solution. It wouldn’t obviously make any sense to apply a total monitoring process on multiple machines when only a simple adjustment on a precise component is demanded.

The introduction of industry 4.0 mechanisms and approaches has to be carefully elaborated so as to ensure an effective operational feasibility of the related programs. So, in order to construct an advanced technological predictive maintenance solution, the organization must describe its use case in detail by defining what it wants exactly to predict, its business benefits, the data available to it, and the hypotheses it acquires. The signals and the failure examples that the company has gathered must be compatible with its use case. Nonetheless, the most elementary requirement to establish properly a predictive maintenance solution is to obtain the right dataset. It would be definitely ideal for the company to hold a dataset that illustrates identifiable machinery deterioration. In fact, for the company to be capable of starting the smart predictive maintenance journey, it has to define clearly the use case then make sure that it has or can produce at least an adequate dataset to match the use case in question. Following so, in order to validate that its dataset acquires the compatible pattern to

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construct the required model, the organization must utilize simple data exploration procedures to indicate whether the data involves any signs of deterioration or deficiency patterns. Once the company holds evidence of clear pattern, it can be qualified to set up the needed machine learning models. (Dhingra, 2018). As for building the adequate learning algorithms in relation to the PdM systems, it is crucial to prepare carefully the required data allowing by that to reveal certain patterns. Once the data has been gathered, it is more vital for the engineers to use the right algorithms for the machine learning models construction. The best way to accomplish a higher degree of accuracy is to choose the metrics that align with the teams’

objectives and the equipment state of degradation.

Reducing the production costs and ameliorating the efficiency requirements have been primary goals for the manufacturing organizations. The most suitable approach to achieve these objectives is to enhance maintenance effectiveness and efficiency by integrating PdM devices and systems into cyber-physical production programs. Nevertheless, this integration is far from being an easy task for the relevant companies. This is why the firms must generate suitable and effective integrated production and maintenance planning systems in the complex cyber-physical production organisms employing mutli-criteria decision making platforms.

Such applications will allow the cyber physical systems to install resilience and inject interoperability into the predictive production systems so that the overall manufacturing productivity could be optimized. Additionally, it will be expected from these applications to endorse the feasibility of the CPS into the predictive maintenance programs.

While discussing the technological improvements in relation to PdM programs, it seems quite important to underline the applications of machine to machine communication and their usage in industrial contexts. Indeed, the emergence of entirely embeddable instruments has elevated the bar on machine to machine technical abilities. Even if this technology isn’t that recent, but its appliance has been relatively restricted until nowadays. M2M technologies engender considerable amounts of information that are mostly orders of magnitude larger than what operators have worked with before. Real-time big data computation capabilities have allowed the flood gates for developing novel predictive analytics into an otherwise uncomplicated data log programs providing real time monitoring and control to take the necessary preventive action in case of any deviations. Condition-based maintenance, smart metering ,usage-based maintenance and load generation are some of the predictive analytics use cases for machine 2 machine. It would be therefore accurate probably to say that the possibilities are various and the industrial firms are working hard in order to collect the benefits through cost-savings and innovative service offerings.(Palem, 2013). Nevertheless, the technical feasibility of M2M tools isn’t simple either and requires some strict managerial and operational preparation to construct the needed base and structures for such innovative changes into the PdM organisms.

Still, it remains essential to keep in mind that each company has its own procedures to follow in order to assure the technical and operational feasibility of its advanced predictive maintenance solutions. It might as well expected for these approaches to evolve or change as their novel systems change or as the market develops innovative maintenance and monitoring tools and programs.

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2.3.5 Business advantages of technological advancements in relation