Discover 5 Predictive Maintenance Startups You Should Watch in 2021

This year, you should know emerging predictive maintenance startups & how they will impact your company. Discover 5 hand-picked, global startups in this data-driven report!

Despite the challenges caused by the COVID-19 pandemic and increasing economic uncertainty, new startups are founded every day. To introduce you to 5 startups that you should watch in 2021, we analyzed 743 predictive maintenance (PdM) startups in total. All of them develop innovative solutions spanning from acoustic prediction methods to photonic sensors.

The 5 promising predictive maintenance startups you should watch in 2021 were hand-picked based on our data-driven startup scouting approach, taking into account factors such as location, founding year, and relevance of technology, among others. The 743 companies that were analyzed for this report are identified using the StartUs Insights Discovery Platform, covering 1.379.000+ startups & scaleups globally. When you are looking for up-to-date predictive maintenance solutions for your innovation units, R&D, or product development department, the StartUs Insights Discovery Platform gives you the most exhaustive collection and ensures you continuously discover new startups, scaleups, and technologies.

Global Startup Heat Map: 5 Predictive Maintenance Startups to Watch in 2021

The Global Startup Heat Map below highlights 5 predictive maintenance startups, developing technology-driven solutions. Moreover, you can explore global hotspots for predictive maintenance startups and even download this graphic to include in your next presentation.


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Lentiq creates Decentralized PdM Data Processing Solutions

Cover-1-Predictive-Maintenance-2021-Startups-StartUs-Insights-noresizePredictive maintenance demands high-end processing power for data collection and preprocessing. Instead of relying on cloud computing, advancements in communication and electronics enable factories to maintain localized analytics hardware and software. Startups build tailored PdM solutions that provide total control and security of the data while increasing the overall equipment effectiveness (OEE).

The US-based predictive maintenance startup Lentiq specializes in building decentralized PdM models. The startup’s solutions gather sensor data from machines, aggregate data, and push part of analytics closer to the edge. Also, the centralized prediction model forecasts outcomes based on historical data while complying with regulatory requirements. Lentiq’s process design helps data teams be independent and focus on data analysis closer to where data is generated. Decentralized PdM improves business operations and reduces overall costs while increasing productivity.

Acoustic Intelligence develops a Non-Invasive Predictive Maintenance Solution

Invasive sensors in continuous machine monitoring interfere with equipment operation, making the process inefficient. Conventional monitoring sensors like accelerometers and force sensors are sometimes invasive and inoperable in hazardous conditions. Electronic innovations enable startups to develop non-invasive alternatives such as infrared or acoustic sensors, among others, for equipment condition analysis while providing better sensitivity at a reduced cost.

Acoustic Intelligence is a Chilean predictive maintenance startup that utilizes non-invasive acoustic microphones to monitor the inherent wear and tear of assets. The startup uses Imager A48, a group of microphones, and computation algorithms that visualize the intensity and location of noise sources. Acoustic Intelligence collects and remotely correlates the data collected by Imager A48, allowing operational and maintenance areas to obtain information at the right time.

Miraex creates Photonic Sensors

Passive sensors that have no electrical circuitry and no metal contents are intrinsically safe for explosive and hazardous areas. In machine condition monitoring, a passive sensor ensures equipment safety and robust operation. Startups develop adequate replacements for conventional sensors in PdM using advancements in the manufacturing and materials industries. For example, photonic sensors detect photons instead of electrons and use the same technology to track machine conditions efficiently.

Swiss predictive maintenance startup Miraex devises photonic sensors to assess machine data. The startup’s continuous monitoring photonic sensors are highly sensitive and highly functional in harsh environments as well. The passive sensors feature a plug-and-play design that allows easy integration into working environments. Furthermore, the startup provides a secure edge Internet of Things (IoT) device for signal processing and transmission along with remote machine learning capabilities. As a result, Miraex’s PdM solutions help the heads of operations, failure engineers, and on-site operators optimize investment, improve productivity, and reduce repair time.

ANNEA provides a Scalable and Adaptable IoT Platform

Machine monitoring, backed by big data analytics, enables PdM startups to understand machine conditions and characteristics. Innovations in IoT, advanced analytics, and computer-aided design (CAD) offer efficient ways to interpret machine data and improve condition forecasting. For example, digital twin-based PdM provides the ability to recognize disruptions, forecast problems, and simulate maintenance scenarios.

ANNEA is a German PdM startup that develops automated drive-train vibration analysis solutions based on artificial intelligence (AI) and physical modeling. The startup utilizes IoT and big data analytics to generate digital twins and enable health condition analysis. ANNEA’s customized dashboards provide a health status overview of all machines and a detailed view of each component. It utilizes vibration and operational data along with condition-based predictive health analysis for automated failure forecasts and predictive condition monitoring.

Hexio provides End-to-End PdM Solutions

Predictive maintenance helps to eliminate downtime and extend the lifespan of machines. However, one of the significant concerns in factories is integrating and managing the PdM system. Startups now develop easily-integrable predictive maintenance systems using plug-and-play architecture and cloud computing. End-to-end PdM solutions provide enhanced analytics capabilities and prediction model efficiency along with overall data security.

Based in the Czech Republic, the predictive maintenance startup Hexio specializes in providing comprehensive PdM solutions for machines. The startup offers certified IoT hardware and a management platform to constantly track and monitor equipment functioning. Hexio utilizes machine learning to automate diagnostics and predictions of failure states. This helps operation managers to reduce maintenance costs and improve productivity.

How will Predictive Maintenance impact your company?

To stay ahead of the technology curve, it is important that you know which technologies and industry trends will impact your company. Explore our free Industry Innovation Reports or let us do the work for you. To keep you up-to-date on the latest technology and emerging solutions, we provide you with actionable innovation intelligence – quickly and exhaustively.

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