Compact Excavators are often rented on an hourly or daily rate. No meters are used, which means that for billing only calendar hours or days are used. For maintenance, the system has an “engine hour” meter, but this gives indicator only when the system is running (idle or driving or operating).
A proposal is to introduce other meters for more precise counters on the actual use of the machine. One sensor is proposed for the solution, which provides a very cheap way of getting much more usage data.
For the rental case a “power by the hour” rate could be more efficient. I.e. the end customer pays for the required usage or wear of the machinery and not just number of hours the machine is reserved. It would give a more fair pricing model, since the real cost of running the machinery is mostly due to maintenance. This would give the user an incitement of taking care of the machine while using it. It also gives a better way to estimate the need for maintenance or to balance out the usage of equipment.
For other cases, a simple sensor could give benefits of getting higher fleet availability, lowering operating costs etc. by doing the following:
Machine health and how to predict asset failure (predictive maintenance)
Prevent or detect abuse
Provide data for warranty models
Provide data for fleet management/optimization
All of the above mentioned points can be addressed with a simple and robust IMU.
Proof of concept thesis
For this proof of concept, we will provide a thesis, to test the data collection and analytic capability of such a system:
“We believe that we can measure how many hours a hammer and tracks / undercarriage has been used on a compact excavator by measuring the vibration pattern”
As proof of concept we want to be able to detect the following states
Engine Off – ID 4001
Idle – low RPM ID 4002
Idle – High RPM ID 4003
Driving – Turtle gear ID 4010
Driving – Rabbit gear ID 4011
Driving – Slalom ID 4012
Hammer – ID 4020
Other states (such as abuse or hard usage) could also be detected.
The Machine Learning approach
A single IMU sensor is installed in the frame of the vehicle. Data is collected with high resolution and high sampling frequency. Data was collected on a small embedded device in the vehicle.
Model creation data
A series of tests with beforementioned states were made. The data was labeled with each state.
After data labeling, a decision tree was created using statistical features of the data.
The decision tree can now be applied to data collected in real time, on the embedded device.
A new series of tests were made. This data was again labeled with each state. Data was collected and parsed with the decision tree generated with the model data from before (with fixed data chunk sizes).
In the figure below, the results from the algorithms can be seen.
On the top row of bars, the data labels (the truth) are seen, colored. In the next row of bars, the detected states are colored. The bottom graph is a visualization of part of the collected raw data.
As seen, the colors match with very high precision. Only in the beginning and end of the states there are small errors. This is most likely because of the data labeling (i.e. as the labels were created manually with a stopwatch they may not be completely timely)
The IMU sensors and embedded device mounted on the Compact Excavator is able to provide data for machine learning and recognition of at least 6 different usage patterns:
The usage information can now be collected, and a “power by the hour” renting concept can be introduced. For example, the renting company can provide an app where the customer can specify how much hammering they want, and how much driving etc. Then a much lower price can be provided. If data is collected and transmitted through GSM, the app can even update in real time, showing usage data.
This means that the operator of the vehicle can in real time see how much usage has been spent. A warning could be provided when i.e. when 80% of the hammering hours have been spent, similar to traveling with a mobile phone abroad and there is a fixed number of Megabytes available.
The whole setup was made within a few hours. Mounting of the system took 30 minutes, collecting model creation data took 1 hour. Creating the models took 30 minutes. And testing the system took another hour. We started in the morning, and before lunch time, everything was mounted, calibrated and validated and ready for use.
This sensor and embedded system provides a very easy way of providing actual and valid usage information on mechanical systems.
It can easily detect more states. The meters provided could also be summarized, which could be used to provide the operator with information on when it is time to replace the hammer – before it actually breaks. The time saving from this alone are enough to pay for the system.
One of the objectives of the MANTIS project is to design and develop the human-machine interface (HMI) to deal with the intelligent optimisation of the production processes through the monitoring and management of its components. MANTIS HMI should allow intelligent, context-aware human-machine interaction by providing the right information, in the right modality and in the best way for users when needed. To achieve this goal, the user interface should be highly personalised and adapted to each specific user or user role. Since MANTIS comprises eleven distinct use cases, the design of such HMI presents a great challenge. Any unification of the HMI design may impose the constraints that could result in the HMI with a poor usability.
Our approach, therefore, focuses on the requirements that are common to most of the use cases and are specific for proactive and collaborative maintenance. A generic MANTIS HMI was specified to the extent that does not introduce any constraints for the use cases, but at the same time describes the most important features of the MANTIS HMI that should be considered when designing the HMI in individual use cases.
MANTIS HMI specifications are the result of refinement of usage scenarios provided by the industrial partners, taking the general requirements of MANTIS platform into account. Functional specifications describe the HMI functionalities, present in most use cases and abstracted from the specific situation of every single use case.
We describe a generic static model that can be used together with the requirement specifications of each individual use case to formalize the structure of the target HMI implementation. The model has been conceived, in particular, with two ideas in mind: (i) to provide means that would help to identify the HMI content elements and their relationships of a given use case and (ii) to unify (as much as possible) the HMI structure of different use cases, which is useful for comparison of implementations and exchange of good practices. When setting up the model structure we follow the concepts of descriptive models applied in task analysis and add specifics of MANTIS, denoted as MANTIS high-level tasks. For each of these high-level tasks, we provided a list of functionalities supporting them.
MANTIS human-machine interaction comprises five main aspects:
Through their user interfaces, several different users within the use case communicate with MANTIS platform, which in term communicates with production assets. Interaction can take place in both directions. Users can not only access the information, retrieved from production assets and stored on the platform but provide an input to the MANTIS system as well. They can initiate an operation which is then carried out by the platform, such as rescheduling maintenance task, or respond to a system triggered operation, for example, alarms. On the other hand, through the MANTIS platform, users can also communicate among themselves. In addition to the straightforward communication in terms of the textual or video chat functions, the users can also communicate via established workflows.
The last but not least main part of the interaction is also the environment. Although it can be treated neither as a direct link between the user and the system nor as a part of communication among the users, the environment can influence the human-machine interaction through the context-aware functionalities.
From the users’ point of view, the human-machine interaction within the MANTIS system supports five main high-level user tasks associated with proactive and collaborative maintenance:
Monitoring production assets;
Maintenance tasks scheduling;
While monitoring production assets, data analysis and maintenance task scheduling are vital for proactive maintenance, reporting and communication enable collaboration among different user roles. Each of these tasks is carried out by a number of MANTIS specific functionalities that can be classified as user input, system output, user- or system- triggered operation. These functionalities should cover all the main aspects of MANTIS human-machine interaction and should also be general enough to be applicable to any MANTIS as well as potential future use case.
MANTIS HMI demonstrator
In the near future, many other features will be implemented, including more widget types, dashboard navigation, search function and sharing of data views. Some context-aware features, such as hidden widgets that appear when needed and suggestions of further user actions based on the usage history, will be implemented as well. In addition, general visual design recommendations such as colours, fonts and widgets positioning, described earlier in the project, will be applied.
The second (sixth overall) full consortium meeting of 2017 was held between 8th and 10th of May. This time it was hosted by VTT at their new Center for Nuclear Safety located in Espoo, Finland. The three day event gathered 65 participants from all of the participating countries. The program was more technologically oriented and contained a long open space session, where partners could present their work within the project. The tight program allowed some time to enjoy the wonderful Finnish spring weather.
Finnish use-case was prominently on display at the Open Space session at the MANTIS consortium meeting. The floor in the first open space room was dedicated to the Finnish use case. Presented were Nome, Wapice, Fortum, VTT and Lapland University of Applied Sciences (UAS). Each partner presented their work done in the Finnish use case. Wapice and Fortum presented their HMIs (IoT Ticket and TOPi respectively). Nome and VTT presented their measurement systems (NMAS and the affordable sensor research respectively) and finally Lapland UAS provided the database and REST interface that allows each partner to share and access data beyond organizational boundaries. The second room had most use cases represented. Of note was XLABs common MANTIS user interface demo that can be connected to the Finnish use case platform.
The Finnish use case is centered on a flue gas recirculation blower located in Fortum’s Järvenpää power plant. The blower is classified as a critical component in the energy production process and is monitored closely. In this use case Wapice, Nome and VTT have all provided their own sensors or virtual sensors to monitor the performance and condition of the blower. In addition, Lapland UAS has a few Wzzard sensors, made by B+B Electronics/Advantech, provide some additional measurement data bulk. However these are not related to the Järvenpää case. This measurement data is stored, using the REST interface developed by Lapland UAS, in the MANTIS database that is based on the MIMOSA data model.
The REST interface and MIMOSA database mapper provides a simple an interface, which is both easy to use and to integrate, between different applications and systems. It provides basic CRUD –functionalities and contains a mapper that maps measurement system specific data formats and structures into MIMOSA compliant data structures to ensure interoperability and compatibility with the MIMOSA data model. It is widely in use in the Finnish use case and research partners from both Slovenia and Hungary have shown interest towards utilizing MIMOSA in their use case.
Goizper and IK4-TEKNIKER will be present at the Hannover Messe from 24 to 28 April 2017 presenting Smart G, a data acquisition module for clutch-brake monitoring.
Clutch-brake systems produced by Goizper are key components in cutting, forming, folding and press machines.
The aim of this presentation is to show how incorporation of the Smart G module can convert a clutch-brake system into a monitorable smart component, which includes self-diagnostics capabilities that can provide information about the current state of the component and predict failures before they occur.
Communications modules incorporated in the Smart G component provides capabilities to:
Remotely monitor the component
Send the data to a cloud platform where all historic data are stored.
Having the data of Goizper’s clutch-brakes fleet on a cloud platform will provide the possibility to use more advanced techniques and algorithms in order to predict failures and/or remaining useful life of key components of the system.
Benefits are two-fold: Goizper will drastically improve the knowledge about their equipment to improve reliability of their products and the maintenance services provided to customers, while customers will benefit from a reduced downtime of the machines and more cost-effective maintenance strategy.
Goizper and Tekniker’s work on failure prediction and diagnosis, as well as cloud platform development, have received funding from the European Union under the MANTIS project.
Figures 3 and 4. Braking and Clutching processes performance
Philips Consumer Lifestyle (PCL) is an advanced manufacturing site located in Drachten, the Netherlands. Our organization falls within the Personal Heath business cluster of Philips, and is primarily concerned with the manufacturing of personal electric shavers.
Electric shavers are comprised of two principle component ‘blocks’: a body and a shaving unit. Each shaving unit contains three metallic shaving ‘heads’, which in turn are composed of a shaving blade (the cutting element) and shaver cap (the guard). The focus of the MANTIS project at PCL falls on the production of these shaver caps.
An electro-chemical process is used in the manufacturing of shaver caps, where an electric current is passed over the raw input material, which is conductive, in order to cut this material into the desired shape. Production of the shaver caps at PCL is fully automated.
Precision tooling is required throughout the various stages of shaver cap manufacturing. At present, these tools are built on-site, and are required to be kept in stock so that replacements are available in the event of tooling malfunctions. Having functional tools available around the clock is essential to meet our goal of 100% ‘up-time’ for our assembly lines. However, this is an expensive approach to resolve the problem, both in terms of the additional equipment required and extensive down-time that results from manual tooling replacements. Therefore, the timely maintenance of these tools presents a challenge.
Currently the maintenance strategy on the production line for shaver caps is a mixture of reactive and preventive maintenance. In line with the Mantis goal, our goal is to transform this towards a predictive or even a prescriptive maintenance strategy. However, this comes with the need for data. In order to perform maintenance on the tooling at exactly the right moment needed, information is necessary about the tooling to make useful decisions.
The data directly related to the current state of tooling (e.g. degree of wear, damages, etc.) is hard to retrieve in some cases, due to process-specific reasons. In our use case the tooling is delicate and very precise (micron range, difficult geometries), which makes frequent measurements of the tooling difficult and expensive in a mass production environment. Currently, there is only indirect data available about the use of the tools in the production machines, but not about the actual state of the tool itself. These data can be used to estimate, for example, the remaining useful life of a tool, but in order to improve and verify the RUL prediction models, more direct data is necessary.
Tool wear sensor
To solve this matter, a collaboration between the University of Groningen and Philips Consumer Lifestyle has been started in context of the Mantis consortium, with the goal to develop a tool wear sensor based on an optical image system. A robust setup with a high-resolution sensor will make detailed images of the individual tools.
The raw images are preprocessed, where the parts of interest of the tool will be cut out of the image and rotated to form the input for a machine learning algorithm. Next step would be to normalize the pictures so they are more or less comparable.
Since we have no baseline, we asked our maintenance engineers (they are the domain experts) to label all these individual images. Together we choose three specific labels: wear, damage and contamination. The input of the maintenance engineers is used to train the algorithm, but also to assess how well these individual pictures are labelled similar when considering multiple engineers.
Currently, over 1500 pictures are labelled in about a month. Initial results seem to indicate that simple machine learning can outperform human labeling regarding tooling deviations.
If results are good, the trained algorithm will ultimately be used with an automatic calculation engine to run new images through the algorithm. This means that we also have to change the way of work, and provide the maintenance engineers with easy-to-use tools to make these new images, as part of their regular maintenance steps. The outcome of the analysis forms an input for determining the remaining useful life of the tool, in combination with both process and quality data.
Nearly 70 participants were able to attend to the three-day MANTIS meeting organized by ACCIONA Construcción S.A. in their premises in Madrid, Spain from the 18th to 20th of January 2017.
The agenda of the meeting was designed keeping in mind the idea of having less informative sessions but more interactive ones to really get fruitful discussions and making decisions for further steps.
The meeting started with a session chaired by the Project Coordinator to let everybody get a precise idea about the status of the project. Then, most of all the use cases presented the last developments, focusing in the data availability and analytics to be used. Following this session, the Open Space took place where several posters were shown and discussed in small groups. In the afternoon, the first parallels sessions started, covering WP3 and WP5.
The second day was very intense. At the beginning, WP3 and WP5 finalized the discussions that started the day before. Then, it was the turn of WP2 and WP4. Regarding the latter, it is worth to say that there were several sessions aiming very specific technical aspects. In the evening, a joint dinner was organized in a very famous place in Madrid, where an impressive flamenco show was performed.
On the last day of the meeting, before the conclusion and wrap-up session, WP2 members kept discussing while WP8 session was running in parallel. Finally, the EB meeting took place.
The continuous and systematic analysis of performance data from the monitoring of operational PV power plants is vital to improving the management and thus profitability of those plants over their lifetime. The article draws on an extensive programme undertaken by 3E to assess the performance of a portfolio of European PV power plants it monitors. The article illustrates 3E’s approach to automatic fault detection. It will explore the various data mining methodologies used to gain an accurate understanding of the performance of large-scale PV systems and how that intelligence can be put to the best use for the optimal management of solar assets.
3E’s work on automatic fault detection and diagnosis has received funding from the European Union under the MANTIS project.
Wapice is a Finnish company specialized in providing software and hardware solutions for industrial companies for wide variety of purposes. We have developed remote management and condition monitoring solutions since beginning and our knowledge of this business domain has evolved into our own Internet of Things platform called IoT-Ticket. Today IoT-Ticket is a complete industrial IoT suite that includes everything required from acquiring the data to visualizing and analyzing the asset lifetime critical information.
Why condition monitoring
In predictive maintenance the goal is to prevent unexpected equipment failures by scheduling maintenance actions optimally. When successful, it is possible to reduce unplanned stops in equipment operational usage and save money through higher utilization rate, extended equipment lifetime and personnel and spare part costs. Succeeding in this task requires deep understanding of the asset behaviour, statistical information of the equipment usage and knowledge about the wear models combined with measurements that reveal the equipment current state of health. Earlier these measurements were carried out periodically by trained experts with special equipment, but now the modern IoT technologies are making it possible to gather real time information about the field devices all the time (i.e. condition monitoring). While this increases the availability of data, it creates another challenge: How to process massive amounts of data so that right information is found in the right time. In condition monitoring process the gathered data should optimally be processed so that amount of data transferred towards the uplink decreases while the understanding of the data increases.
This article describes how modern IoT technologies help in condition monitoring related processes and how data aggregation solutions makes it possible to share condition monitoring related information between different vendors. This further improves operational efficiency by enabling real time condition monitoring not only in asset level but also in plant or fleet level, where service operators must understand the behaviour and available lifetime of assets coming from different manufacturers.
WRM247+ data collector for edge analysis
The first link in the condition monitoring chain is the hardware and sensors. In order to measure the physical phenomena behind the wear of the asset a set of sensors is required to sample and capture the data from monitored devices. This data must be buffered locally, pre-processed and finally only the crucial information must be transferred to the server, where the physical phenomena can be identified from the signals. Depending on application area a different types and models of sensors are required to capture just the relevant information. Also depending on the physical phenomena a different kind of analysis methods are required. Due to these reasons the various measurement systems have so far been custom tailored according to the target. This approach of course works, but designing custom tailored measurement systems is time consuming and expensive. Our approach to overcome these problems has been to implement such IoT building blocks that adapt into wide variety of purposes and can be easily and flexibly taken into use. One of the cornerstones in our system is the flexibility and user friendliness.
In the hardware side our IoT platform offers several approaches. WRM247+ measurement and communication device is our hardware reference design that allows connecting a wide variety of industrial sensors using either wired or wireless communication methods and also provides local buffering and pre-processing of data as well as communication to the server. Examples of supported standard protocols are CAN, CANOpen, Modbus, ModbusTCP, 1-Wire and digital/analog IOs. This device is an excellent starting point for most common industrial measurement purposes.
In Mantis project Wapice has been investigating the interoperability of the wireless and wired sensors. In use case 3.3. Conventional Energy Production we will demonstrate the fusion of the wireless Bluetooth Low Energy technology and wired high accuracy vibration measurements. In order to achieve this we have built a support for connecting IEPE standard vibration sensors to the WRM247+ device. The device supports any industrial IEPE standard sensor, which makes it possible to select a suitable sensor according to the application area. Additionally we have also built a support for connecting a network of BLE sensors to the device. In use case the purpose of this arrangement is to gather temperature information around the flue-gas circulation blower using the wireless BLE sensors and perform vibration measurements from the rolling bearing. The temperature measurements reveal possible problems e.g. in lubrication of the bearing and possibly allow actions to be taken before a catastrophic failure happens.
In case WRM247+ device is not suitable for purpose, it is possible to integrate custom devices easily into IoT-Ticket using the REST API available. For this purpose we provide full documentation and free developer libraries for several programming languages. The list of available libraries includes for example C/C++, Python, Qt, Java and C#. Other integration methods include for example OPC or OPC UA and Libelium sensor platform, that supports e.g. wireless LoRa sensors. In addition Wapice has a long experience in designing machine-to-machine (M2M) solutions including PCB layout, embedded software design and protocol implementation, so we also offer you the possibility to get tailored Internet of Things hardware or embedded software that fully suit your needs.
Iot-Ticket portal for Back-End tools
In the back-end side IoT-Ticket provides all necessary tools for visualizing and analyzing data. Our tools are web based and require no installation: Simply login, create and explore!
The Dashboard allows users to interact securely with remote devices, check their status, view reports or get status updates on the current operational performance. It can be utilized in various scenarios – e.g. vehicle tracking, real time plant or machinery monitoring and control. As many Dashboard Pages are available the user can switch between different contexts and drill into information starting from enterprise level to sites, assets and data nodes. The Dashboard also includes two powerful tools for content creation: Interface Designer and Dataflow Editor.
Using the Interface Designer user can raw new elements or add images, gauges, charts, tables, Sankey diagrams, buttons and many other elements onto the Dashboard. Those elements can then be easily connected to data by dragging and dropping Data Tags onto them.
The Dataflow editor is an IEC 61131-3 inspired, web-based, graphical block programming editor that seamlessly integrates to the Interface Designer. A user can design the dataflow by connecting function blocks to implement complex logic operations which then can be used to execute control actions or routed to user interface elements for monitoring purposes.
In Use Case 3.3. Conventional Energy Production Wapice – together with Finnish partners – demonstrates Cloud-to-Cloud integration in Mantis platform using the IoT-Ticket platform tools. In this use case LapinAMK and VTT have jointly setup a Microsoft Azure based MIMOSA data aggregation database. The plan is to share condition monitoring related KPI information through MIMOSA database, which allows sharing data through REST API. Devices may either push data directly to MIMOSA or through local clouds.
IoT-Ticket allows communication to REST sources using Interface Designer graphical flow programming tools. Getting data from REST sources is done by simply creating a background server flow that contains a trigger and REST block. The REST block is configured with username and password that allow authentication to REST source, source URL and REST method that contains XML/JSON payload. From the REST response the data value is parsed and output to data charts or forwarded to further processing. Additionally virtual data tags allow forwarding the data into IoT-Ticket system. By configuring the flow to be run in server mode, the flow is run silently in background all the time. The operation interval is configured using the timer block, which fires the REST block in certain intervals. An example video below shows how Cloud-to-Cloud communication between MIMOSA and IoT-Ticket is established in Use Case 3.3. In this example video sinusoidal test data originates from LapinAMK enterprise and tachometer RPM reading from system under test using WRM247+ device.
The reporting and analytics tools add on to the platform features. The report editor integrates seamlessly into the Dashboard and offers a user the possibility to create or modify content. The user can draw new elements or add image, gauges, charts, tables, Sankey diagrams, buttons and many other elements onto the report. Those elements can then be easily connected to data by dragging and dropping Data Tags onto them. Analytics tool is also integrated to the Dashboard and supports you in understanding your data better.
Benefits of IoT in condition monitoring
Typically condition monitoring related data has been scattered around in separate information systems and it has been very hard or even impossible to create a single view of the all relevant data or correlate between information scattered in different databases. MIMOSA is an information exchange standard that allows classifying and sharing condition monitoring related data between enterprise systems. It answers the data sharing problematics by allowing aggregation of crucial information into single location in a uniform and understandable format. When interfaced with modern REST based information sharing technologies that utilize for example JSON or XML based messaging it is surprisingly easy to collect and share crucial information using single aggregation database. When accompanied with modern web based Industrial IoT tools it is then easy to visualize data, create reports or perform further analysis using only the crucial information available.
In this blog posting I have highlighted some examples how Industrial IoT building blocks help you to gather relevant condition monitoring information, create integrations between data sources and aggregate business relevant information into single location. Focusing on the crucial information allows you to understand better your assets and predict the maintenance needs. This is needed, when optimizing the value of your business!
With SMARTLINK monitoring program, Atlas Copco makes use of connectivity data and data intelligence to help customer to keep up their production uptime and to improve, when possible, energy efficiency.
With approximately 100,000 machines connected with SMARTLINK, Atlas Copco makes compressors in the field communicate directly with the back office and their service technicians.
Customers become more proactive, planning is more efficient and reliability of the compressed air installations is better than ever before.
Customers of SMARTLINK get a monthly overview of machine information, including running hours and the time left before service, thus allowing them to order a service visit at the right time, maintaining maximum uptime and energy efficiency.
With SMARTLINK they can closely follow up on machine warnings via email or SMS. With this information they can take the necessary actions to prevent a breakdown.
With the MANTIS project, Atlas Copco will take proactive maintenance to the next level, by:
predicting the remaining useful life of consumables and components that are subject to wear
detecting upcoming problems or inefficiencies before they deteriorate
remotely diagnosing the root cause of an unplanned shutdown
Moreover, in order to reduce communication costs, smart sensing technology is being investigated, or how local preprocessing of information can significantly reduce the amount of data to be transmitted.
A major challenge for Atlas Copco is the huge variety of compressor types and operating conditions. To process this enormous amount of information, self-learning techniques are combined with physics-based compressor models. Eventually, these will enable the discovery of new patterns in data, collected on a worldwide scale.
The ultimate goal is to translate these data into actionable information for the global service network.
Service interventions will be planned even better and will be shorter and more efficient. Problems will be fixed in one visit, as technicians will know in advance what to do and what parts to bring.
For the customer, this means no unnecessary maintenance, less planned or unplanned downtime, therefore achieving maximum productivity.
Cyber-Physical Systems (CPS) are often very complex and require a tight interaction between hardware and software. As it happens in almost any software systems, also CPS generate different kinds of logs of the activities performed, including correct operations, warnings, errors, etc. Frequently, the logs generated are specific to the different subsystems and are generated independently. Such logs contains a wealth of information that needs to be extracted and that can be analyzed in different ways to understand how the single subsystem behaves and even retrieve information about the behavior of the overall system. In particular, considering the generated logs, it is possible to:
Analyze the behavior of a single subsystem looking at the data generated by each one in an independent way;
Analyze the overall behavior of the system looking at the correlations among the data generated by the different subsystems
Such data are very useful to understand the behavior of a system and are often used to perform post-mortem analysis when some failures happen. However, such data could also be used to understand in a more comprehensive way how the system behaves through a real-time analysis able to monitor continuously the different subsystems and their interactions. In particular, it is possible to focus on preventing failures through predictive maintenance triggered by specific analysis.
Making predictions about system failures analyzing log files is possible but such predictions are strictly related to some characteristics of such files. In particular, some very important characteristics are: data generation frequency, information details, history.
The data generation frequency needs to be related to the prediction time and the time required to take proper actions. For example, if we need to detect a failure and take proper action in a few minutes, we need to use data generated with a higher frequency (e.g., in the scale of the seconds) and we cannot use data generated with a lower one (e.g., in the scale of the hours). This requirement affects the ability to make predictions and their usefulness to implement proper maintenance actions.
The information details provided need to include proper granularity and meaningful massages. In particular, it is important to get detailed information about errors, warnings, operations performed, status of the system, etc. The specific details required are tightly connected to the specific predictions that are needed. Moreover, the finer the granularity of the information, the higher are the chances of being able to create a proper prediction model.
High quality data history is required to build proper prediction models. However, just having a large dataset is not enough. Historical data need to be representative of the operating environment and include all the possible cases that may happen during operations. In particular, it is required to have information about the log entries and the actual behavior of the system to create a reliable model of the reality.
The requirements described are just a first step towards the definition of a proper predictive maintenance model but they are essential. Moreover, the proper approaches and algorithms need to be selected based on the specific system and the related operating conditions.
MANTIS; Cyber Physical System based Proactive Collaborative Maintenance.
This project has received funding from the ECSEL Joint Undertaking under grant agreement No 662189. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Finland, Denmark, Belgium, Netherlands, Portugal, Italy, Austria, United Kingdom, Hungary, Slovenia, Germany.