Technological developments in CM

Just as multiple fields such as telecom, data analytics, infrastructure and smart instruments have been enhanced by innovative technological tools, the exact same thing has been said about condition monitoring. Constraints of human inspection including diminished effectiveness, fatigue, high level of human mistakes occurrence, inconsistency and individual disparity have been essential drivers to boost the development of CM techniques and devices.

Typically, the drive has been accelerated by the technological progresses such as cost-effective, rapid and rigorous inspection mechanisms and examination means provided by machine conditioning monitoring technology. The advancements of sensors, internet of things (IoT), analytics, electronics, software and machine to machine (M2M) communication have been other factors to lead the improvement of condition monitoring systems. Such developments have brought up some meaningful advantages of scalability, remote operability, real time data and automation resulting in upgraded performance, efficiency, maintainability, accuracy, safety and decreased human intervention and by that reduced potential failures. It seems therefore vital to discuss the current technological developments in relation to condition monitoring in today’s industries.

One of the currently broadly utilized technological ameliorations in CM is the advanced software. As sensors are placed to register the multiple parameters of the operating machinery and their conditions, it appears more crucial to use the suitable application to gather the information and report the needed actions. For this reason, the establishment of condition monitoring software has been expanding speedily within multiple industrial environments as manufacturers and managers search for easier and more effective methods for data interpretation and analysis after its eventual collection by CM systems to take the necessary actions upon it. Such software doesn’t only concentrate the condition monitoring data but can as well support the scheduling and delivery of an entire condition monitoring structure from scratch. Where should the sensors be implanted? What should they be measuring? How should they be calibrated? What kind of warnings and alerts should they be sending and to where? All of these requests can be answered by condition monitoring software allowing industrials by that to assess the facility’s operations progress and the equipment state of health. Usually CM software is constantly linked to another technological innovation that is related to remote control devices. Once CM remote has been put in place, the software persists to behave as its hub focusing and collecting all the data inputs being reported by the used sensors into a single focal repository permitting to undertake deep data analysis and determine the required corrective actions.

When discussing the novel advancements in CM techniques and methods, it is essential to mention industry 4.0 as a central part of the CM improvements. Indeed, condition monitoring experiences significant transformations regarding the way its devices are fabricated and designed due to digitization of manufacturing. This progression is so compelling that is being

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named industry 4.0 to illustrate the fourth evolution that has occurred to manufacturing. From the first industrial revolution that was about the mechanization through water and steam power to the second one consisting in mass production and assembly lines through electricity, the fourth industrial revolution has takes what has been initiated during the third revolution with the application of computers and automation and worked on ameliorating it with smart and autonomous mechanisms fueled by data and machine learning. When computers were introduced in industry 3.0, it was a breaking through innovation that brought up mult iple opportunities to stakeholders in diverse domains. Today and in the future as the fourth revolution extends, computers are affiliated and communicate with each other so as to make decisions without human involvement. An amalgam of cyber-physical systems (CPS), the Internet of Things, Artificial Intelligence, Cloud computing and the Internet of Systems make industry 4.0 possible and more efficient. With the implementation of such concepts in industrial facilities, the access to information will be much easier and its analysis will be more accurate. Smart machines make our firms better, more productive and less wasteful. Thusly, the real power of industry 4.0 relies on the high reliable network of machines that are interconnected and do share data. As an obvious outcome, companies have started noticing the importance of introducing industry 4.0 in their CM systems and devices. Industry 4.0 gives indeed the relevant company the opportunity to enhance the quality of their daily CM operations rapidly and effectively by letting employees and managers discover what actually requires their attention. (Bernard Marr, 2018). Moreover, it allows machines, sensors, devices and people to communicate and exchange properly information. Another advantage of industry 4.0 introduction in CM systems is the ability to have information transparency where systems are capable of aggregating data collected from the physical world into a virtual duplicate. Additionally, it provides vital technical assistance to workers in order to help them in solving issues and taking the right decisions. While talking about decisions, it is primary to mention that with the application of industry 4.0 tools we talk more about decentralized decisions giving by that the related systems the ability to take their own decisions through mainly employing artificial intelligence to execute tasks with a minimal human interference.

(Yash Mehta,2018).

When we consider any given industrial facility that employs CM techniques as a central part of the fourth industrial revolution with all its segments, we usually contemplate the use of reliable wireless sensors with pre-engineering monitoring solutions that can spontaneously collect, analyze and report maintenance technicians and operators at the start of a developing condition. This is industry 4.0 condition monitoring at its best. Following the trend of industry 4.0, automation in multiple manufacturing schemes has generated the usage of intelligent condition monitoring systems, which have proven themselves essential for upgrading productivity, operability and availability of the relevant production mechanisms. In order to develop similar intelligent systems, semantic technologies are of a preeminent importance.

The manufacturing industry of CM systems is benefiting from a trend of automation in data exchange processes. A central part of technological advancements in condition monitoring is Cyber Physical Systems (CPS) which make industrial facilities more productive and more capable of operating with autonomy and intelligence. The automatic interchange and assessment of information offer crucial opportunities for industrials mainly manufacturers to optimize the production processes. Gathering data from diverse parts in a production line and

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assessing them in an expandable Cloud infrastructure can automatically enhance the performance, productivity, maintainability, reliability and availability of the production systems in heterogeneous environments. The appliance of CPS in industry 4.0 has led to the rise of the so named “Smart Factories”. With the adoption of Cyber Physical Systems, information within Smart Factories is closely controlled and regularly synchronized between the manufacturing organizations and the cyber computational space. Hence, in order to ameliorate the quality, availability and productivity levels of productions mechanisms, CPSs are supplied with advance technologies to acquire the needed abilities to self-awareness and self-maintenance. These capacities are able to provide significant resilience, automation, productivity to the CPSs and facilitate the establishment of intelligent condition monitoring systems (ICMs) which can offer optimal solutions to manage the large amounts of data efficiently, to perform prognostics in an intelligent manner and procure predictive decisions about equipment deficiencies and machines degradation trends. Figure 4 illustrates a schematic view of a condition monitoring task based on an intelligent condition monitoring system performed by a three-layer cooperation between cyber space and the physical environment. (Bertrand de Beuvron, Cao, Giustozzi, Reich and Zanni Merk, 2019).

Figure 4: A condition monitoring task performed by an ICMS, based on a cyber-physical approach.

(Bertrand de Beuvron, Cao, Giustozzi, Reich and Zanni Merk, 2019).

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As we can notice on the figure above there are three main layers:

 At the bottom there is the physical space layer, where information is collected from sensors implemented on the machines and tools. Additional information might be gathered as well from the industrial environment, the equipment itself and the operators’ experience. Following that, all the information is visualized on the relevant screens and devices.

 The gathered sensor data are therefore stored and processed at the middle layer which is called the Cyber Physical Interface. At this layer, statistical methods such as large data algorithms utilize the stored data in order to comprehend in a better way the industrial and manufacturing processes and to learn from the operators’ expertise.

 Concerning the cyber space, it procures decision-making about equipment defects prognostics and maintenance. Machinery deterioration models that are derived from the mining of sensor data are acquired in the cyber space. Thus, ontologies are employed to generate the knowledge base of equipment health, and ontological reasoning approaches are used to predict machinery degradation, damage, and quality loss or maintenance requirements in the future. These predictive outcomes are transmitted downwards to the physical space so as manufacturing organizations would make the suitable responses in a timely, efficient and accurate manner. (Bertrand de Beuvron, Cao, Giustozzi, Reich and Zanni Merk, 2019).

In order to develop such an intelligent condition monitoring system, the first thing to do is to establish and define domain knowledge. Indeed, information that can come from distinct sources can hold various meanings according to the relevant domains and contexts making by that the task of working on harmonizing the data an extremely difficult job. To overcome this challenge, multiple companies have come today to the conclusion that technology have to work harder on constructing semantic doctrines in order to deal with the collected data by ICMSs. They are building for these reasons shared and rigid and equipment understandable vocabularies with solid structures. Typically, semantic technologies, mainly ontologies, seem to be the most appropriate solutions to solve the semantic interoperability issue. In fact, the usage of ontologies can provide a better comprehension and coherence of semantics among the diverse participants within a domain. (Bertrand de Beuvron, Cao, Giustozzi, Reich and Zanni Merk, 2019).

Another key element of the technological improvements is the usage of the Internet of Things in offering much more reliable and advanced solutions in CM techniques and devices. The IoT which is an essential component of industry 4.0 is chiefly characterized by connected instruments. The adoption of IoT doesn’t only support the internal operation, but through the usage of cloud environment where information is stored, machinery and activities are improved by putting in leverage the insights of other parties employing the same machinery or allowing smaller organizations get the access they need to the technological tools that they couldn’t have access to on their own. (Marr, 2018).

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The use of IoT brings so many benefits to the industrial facilities in question; principally more efficient cloud storage for large amounts of data. In fact, every company has a specific data center and owns multiple dedicated servers to bring in data and process it. Hence, IoT leverages computing cloud and keeps considerable amounts of data in the cloud. As different kinds of machinery are connected through IoT, a huge amount of data is produced. By leveraging industrial IoT, industrial firms can store larger volumes of data and still optimize and enhance the storage capacities. IoT has had also a valuable impact on condition monitoring systems that is increasing the ability of the company’s machine learning. We are aware that machine learning necessitates huge content of information. Let’s consider the example of a predictive instrument which recognizes the vibration in a drilling machine and analyzes the results to determine the existing cracks. A proper machine learning will require data from more than 100 crackings. So, if a company derives the data from one machine, it will take several years for the process to be achieved. While with the use of IoT, the process will last less than a year as IoT can simultaneously collect data not just from one machine but from many. Additionally, taking data from distinct equipment will lead to a diversification of information sources and by that to more accurate results improving eventually the reliability, functionality and precision of the predictive model. Besides, IoT predictive maintenance tactics are much more performing. Indeed, the IoT predictive maintenance tactics observe carefully and closely the machinery health state and subsequently detect in real times and exact figures the variations in the equipment condition parameters that could be pinpointing to a developing problem. IoT ensures therefore a rapid and efficient predictive maintenance in condition monitoring. Another advantage of Iot is the remote condition that has been significantly improved lately and still is. With IoT advanced solutions, companies are today able to monitor accurately thousands of machines from a specific location. It does no longer require the physical access to the equipment which can be extremely costly and time-consuming affecting negatively the productivity level and downtime. For instance, multiple firms in the oil and gas industry mainly as well in the electric sector exploit IoT remote controlling advanced devices and systems for better condition monitoring of their machines as they facilitate the monitoring of remote installations especially equipment such as pipelines and drilling rigs. The collection of data itself is performed rapidly and accurately then transferred to the cloud for better processing. One of the industrial sectors that had greatly benefited from the condition monitoring IoT is the automotive sector. Based on the statement of the Automotive Fleet per driver automobile, downtime deduces in an average loss of

$79.32 per hour. The damage doesn’t involve the costs of repairing. Condition monitoring IoT allows thus to decrease the downtime and to enhance the usage of maintenance resources.

Condition monitoring IoT permits as well to monitor the related vehicle based on the study of parameters such as vehicle vibration, engine temperature and fuel consumption. It measures typically the potential failures prior to their occurrence. The condition monitoring IoT technological sensors determines exactly when the vehicle condition goes beyond its standard functioning point. Other examples can be obviously point out when it comes to the adoption of CM IoT programs or devices. (Singh, 2018). Thanks to all the profits CM IoT has proven, companies are now convinced more than ever that the Internet of Things can considerably reduce equipment downtime, collect exhaustive amounts of relevant data, detect in more

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accurate and timely manner the possible deficiencies before they take place and manage more efficient controlling tasks.