PREDICTIVE AND CONDITIONAL MAINTENANCE
Monixo is a complete solution for monitoring the operation of industrial assets and for anticipating failures on these in real time.
Monixo is a complete solution for monitoring the operation of industrial assets and for anticipating failures on these in real time.
Monixo provides a complete, efficient and reliable solution to effectively anticipate failures. Thanks to continuous data acquisition and analysis, Monixo beyond alerting teams in case of fault detection, ensures anticipating these. Monixo allows acting in time in order to avoid the cumbersome and costly failures, but never too early to avoid the expense and unnecessary downtimes.
Monixo offers the ability to integrate existing acquisition systems and instrumentation devices (existing sensors) at the user's place.
As well for the provision of data and analysis results: in addition to its website and mobile applications, Monixo easily integrates with existing maintenance management systems (GMAO or ERP).
This will add proven and critical value to proactive maintenance, but especially not to complicate or disrupt the processes and habits of end users by leveraging the existing.
From manufacturing SMEs to industrial groups with numerous and sometimes geographically distributed assets, Monixo responds to needs and technical requirements (types of connectivity, sensors/relevant data to be collected, security...). Analysis of the data flows in real time by the learning processor allows their immediate processing, which gives the processor the ability to analyze mass data with high performance, combining processing speed and reliability of forecasts.
The modular architecture (standalone components, one vis-a-vis the other ones) ensures the solution a rapid deployment (within a few minutes) and the ease of use (plug-and-play). Battery optimization, analysis algorithms and data collection, types and network topologies... All this complexity is fully integrated into the solution in order to provide a simple, transparent interfacing between the service technician and supervised assets. In addition to the usability and intuitiveness of these applications, Monixo allows users to access the data as directly as possible.
Monixo is an ecosystem dedicated to connected and proactive maintenance, the one of the factory 4.0 . The architecture meets the requirements of security and confidentiality, which are subject to maintenance 4.0 systems, and also to the requirements for interoperability. Indeed, the modular and standardized design of its components offers them great autonomy vis-a-vis each other and allows them to interface easily with other existing systems (sensors, CMMS). Its API also provides to the solution additional integration flexibility.
Wireless acquisition, high-precision and two-way, communicating on a high speed network, medium-range, low-consumption, but also on long-range networks (LoRaWAN) systems for geographically distributed assets
In charge of the provision and storage of data, it interfaces third party's systems through its APIs
Guarantor of automatic data analysis and prediction with the patterns of extraction
The acquired data and analysis are made available on the Monixo platform via the web and mobile applications
Relevent data are the key for predictive maintenance
GET A KITMonixo was designed on the assumption that all industrial assets have signs, visible or not, of degradation announcing the failure. Connected sensors allow to measure degradation. The measured data is made available and stored in the cloud, where the learning processor performs its analysis. The result of this analysis is available on the platform with two purposes: continuous monitoring of optimal thresholds of operation, more commonly known as conditional maintenance, and anticipating failures through continuous analysis of the state of assets, predictive maintenance To this comprehensive approach to predictive maintenance, we named it CAP approach. The example below is an illustration of this approach applied to a pellet press of one of our agribusiness customers.
Within seconds, monitors are set up on the machine to be supervised on the basis of a detailed instrumentation plan previously defined. In the case of this press, instrumentation relates to bearings and drums (engine at 1500 rev/min). The monitor is fixed by its magnetic base in this use case.
From the start of the monitor, measurements are collected and instantly available on the platform. On each one of the instrumented subcomponents, the relevant measurements are acquired in order to have a continuous monitoring of the degradation of the press: vibration (global level and FFT), infrared temperature, ultrasound, the current intensity (via an amperometric sensor in the cabinet)...
Once acquired, the data is analysed by integrating the thresholds defined by the maintenance operators from the platform or those defined by the existing standards on the machine in question (conditional maintenance). Alerts are then sent as a function of the selected stroke.
Monitors bi-directionality allows on measures such as the vibration of acquiring spectra, which are automatically analysed and interpreted with detailed monitoring of the evolution of detected anomalies. These analysis, as well as the historical data, are available and can be exported in several formats (XML, CSV, or PDF).
Monixo, through its automatic learning processor follows several failures signatures on the machines in order to automatically extract reliable failures scenarios. The analysis of these patterns allows to detect and track failure at the first known signs of abnormality.
Gradually, prediction gets refined; characterization of failures corresponding to it becomes more formal. The concerned measures, as well as the type of characterized upstream failures allow then to act in a targeted way and to optimize actions and resources: act early enough to avoid cumbersome and costly failures, but never too early to avoid the expenses and unnecessary downtimes
Monixo fits many sectors of the industry, respecting the specificities and constraints of each business. The solution is deployed in the energy, transportation, or manufacturing industries where it provides adequate answers to predictive maintenance needs.
In a railway technical center, the train ferry is one of the most important equipment. It collects and dispatches railroad car from the railways to the workshops and vice versa. This case study present the result of the analysis of vibration, gyroscopic data coupled with IPS (indoor positioning system) informations in order to anticipate mechanical failures on specific sections of the rail and failures on specific components (misalignment fault or crab steering for example). Details
CNC appear to be some of the most "predictive maintenance friendly" type of machine as they mainly produce huge amount of data and are at the same time high precision systems which are sensitive to wear and configuration changes. But the main issue is that a CNC machine is a mix of different components which each undergoes wear and damage that can affect other (more critical) components. So to predict failure on critical components of a CNC is important to create model that can understand links between components and how they can impact one another : for example dissociates a tool wear with the spindle wear on machining center when we only got vibration/current data on the spindle (what is the case most of the time).
Details
The shaft of the crusher was systematically changed after operating 500 hours without knowing if the component needed to be changed. This short preventive maintenance interval is due to the fact that the machine crushes a very abrasive material.Thanks to vibration, acoustic emission and temperature data, the wear models of the shaft were created and the Remaining Useful Life (RUL) of the shaft was calculated in order to optimize preventive replacement time which is most of the time greater than 500 hours (715 Hours on average →+43 % of production time). Details
Criticality of an air handling unit is obvious when we know that it takes care of the quality of the air we breathe. Filters, Motors and Chassis are the main critical parts that are monitored with IIOT to prevent their failures several weeks in advance. Details
For many job sites, especially in the mining and tunneling industries, pumps are crucial to staying on schedule — when they start to fail, the entire project can be delayed. This case study present the result of the analysis of current, vibrations and temperature in order to anticipate pump failure. Details
70 to 80% of the company turn over is realized in winter when electricity is at his highest price, so the goal of this project is to anticipate all failures during this critical period an ensure the maximum reliability of the hydro electric plant. Details
4 old dry coolers (HVAC) on a high power transformer are monitored with Monixo's IIOT systems. These dry coolers have a crucial role, if they stop cooling, the power transformer could overheat then shutdown and even worse can explode. Any of this issues will have the following consequences : thousands of houses without electricity and the deterioration of the equipment causing unavailability for several days. Details
Another important part of the industry is his supply chain which should be efficient and reliable. This case study is based on data collected from the Onboard Diagnostic (OBD) system, GPS and additional sensors on over 50 Renault trucks belonging to a mining company. The predictive models designed help to anticipate most of the critical engine failures the trucks used to face in the past and by this way, drastically optimized their reliability. Details
MetalWorking Fluids (MWF) are used to cool and lubricate machines and tools. Most of the time, MWF are oil-in-water emulsions (1% to 10% of oil concentration) with characteristics depending on type of oil, material to be worked, etc. Online and accurate control of the MWF help to anticipate poor quality in a machining process. This control is based on periodic measurements of oil concentration, pH, temperature, conductivity,... in order to proactively compensate inevitable changes due to water evaporation, bacterial attack, oil adhesion to metal parts, etc. Details
Flow lines, Vacuum voids, Burn marks are part of the most common quality defects related to injection molding. They can be caused by problems related to the molding process or material use and can be sometimes prevented by adjusting some parameters (flow rate, temperature or pressure) . These problems can lead to unsellable goods and product returns. Details
Take advantage of hundreds of accurate predictive models. This will boost your predictive maintenance project and make you gain time by avoiding long learning and model training stage
This kit contains Industrial IoT which send data to Monixo 's cloud or your own private cloud.
Those data are analyzed in order to anticipate failure of the monitored machine
Yes! Predictive through reliable anticipation of recurrent failures and demonstration of uncharacterized defects based on measures of our monitors or your own sensors.
According to the systems, failures are between 80% and 90% known, recurrent. Beyond the elimination of these recurrent failures, Monixo allows to anticipate abnormalities related with new failures featuring maximum entropy and reliability in both cases.
We do not have a problem with that. Data is secure and confidential on our servers. But whether it is hosted with us or entirely with you, makes no difference.
The integration of the existing is a part of our core capabilities. Your sensors will therefore be seamlessly integrated to our analysis while ours can be integrated to your systems in order to offer new preventive capacities.
Our monitors are robust, resilient and waterproof. The type of material components can adapt to the use they are intended (very high temperatures, floodable, high pressures...). Moreover, their geolocation facilitates their use in active geographically dispersed and/or difficult to access assets.
The monitor is a multi-measures acquisition system. It embeds a plurality of sensors selected from a thematic logic for an electromechanical system, it would be a relevant example of measuring vibrations, ultrasound, and contact temperature while for the ambient-called supervision it would be useful to supervise a gas-specific content (flammable, toxic...) or ultrasonic signals. The flexibility offered by our monitor allows to totally customizing (materials, sensors, information) with you, in order to collect the relevant data.
Having access to a vast amount of related data through the cloud, vehicles calculate failure patterns and alert their driver of a possible maintenance issue shortly. French startup Monixo provides real-time monitoring and tracking of vehicles to identify and anticipate failures.
Startus Insights "Top 4 Out Of 500 Big Data & Analytics Startups In Automotive"
Notre équipement permet, par exemple, de prévenir un responsable que dans 3 semaines, le roulement de sa machine va tomber en panne. C'est ce qu'on appelle la maintenance prédictive et conditionnelle. Il s 'agit d'un ensemble de solutions qui permettent aux équipes de maintenance d'anticiper les pannes.
LetsGoFrance.FR "Monixo, un nouvel expert français en maintenance prédictive"
Pour les paliers, les arbres tournants, les roulements..., le processeur d'apprentissage permet d'anticiper l'évolution de l'état des machines, et par conséquent de réduire considérablement le nombre de pannes, et donc d'augmenter la productivité
L'Embarqué "Avec ses capteurs sans fil, Monixo amène la maintenance conditionnelle connectée au plus près du terrain"
Monixo propose dans ce cadre une solution innovante permettant la supervision et le monitoring de l'ensemble des moyens de production de l'entreprise(véhicules, machines, ascenseurs, ...) via une seule et même interface accessible depuis PC, tablettes et mobiles.
Automation Sense "Maintenance et objets connectés : la solution de Monixo"You have questions about our solutions? We aim to provide you the answers.
