Staying ahead of the technology curve means strengthening your competitive advantage. That is why we give you data-driven innovation insights into predictive maintenance solutions. This time, you get to discover 5 hand-picked predictive maintenance startups.
Out of 976, the Global Startup Heat Map highlights 5 Top Predictive Maintenance Startups
The insights of this data-driven analysis are derived from the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, covering 2 093 000+ startups & scaleups globally. The platform gives you an exhaustive overview of emerging technologies & relevant startups within a specific field in just a few clicks.
The Global Startup Heat Map below reveals the distribution of the 976 exemplary startups & scaleups we analyzed for this research. Further, it highlights 5 predictive maintenance startups that we hand-picked based on criteria such as founding year, location, funding raised, and more. You get to explore the solutions of these 5 startups & scaleups in this report. For insights on the other 971 predictive maintenance solutions, get in touch.
Hark Systems optimizes Industrial Asset Performance
Founding Year: 2016
Location: Leeds, UK
Partner for: Energy & Equipment Condition Monitoring
Industries: Industry 4.0
British startup Hark Systems provides Hark Platform, a cloud-based sensor platform that connects, analyzes, and optimizes industrial assets, buildings, and energy in real-time. The startup’s other platform, Hark Connect, connects industrial devices, assets, and sensors on the edge, cloud, and on-premises. These solutions allow energy managers and asset operators to gain visibility into industrial operations as well as proactively maintain and repair damages before failures occur.
IoTank improves Onsite Wastewater Management
Founding Year: 2019
Location: San Francisco, USA
Partner for: Tank Monitoring
Industries: Wastewater, Utilities
US-based startup IoTank provides predictive maintenance for onsite wastewater management. The startup deploys the internet of things (IoT)-based advanced sensor technology and machine learning (ML) to optimize on-site wastewater management. The sensors measure waste levels and wastewater usage, providing real-time data. The data collected from the sensors are used to predict maintenance dates, failures, and risk levels of each system, allowing plant operators to minimize downtimes and maintenance costs.
SecuriThings facilitates Large-scale IoT Deployments
Founding Year: 2016
Location: Ramat Gan, Israel
Partner for: Risk Detection, Physical Security Systems
Industries: Financial Services, Ports, Building Management
SecuriThings is an Israeli startup that improves the operational management of large-scale IoT devices. The startup’s platform deploys machine learning to detect and mitigate threats across deployed devices as well as provides inventory management for IoT devices. It monitors device health by gathering metadata and storage consumption along with other parameters. Moreover, the platform leverages advanced analytics to detect abnormal behavior like multiple streams or increases in processing power consumption. This enables large facilities, such as ports, airports, educational institutions, and municipalities, to predict failures at the individual device level and implement predictive maintenance.
Rakr enables Energy Optimization
Founding Year: 2016
Location: Toronto, Canada
Partner for: Energy Consumption Monitoring
Canadian startup Rakr provides a predictive maintenance system for farm applications. The startup’s solution, NeatMeter, enables farmers to monitor energy consumption in real-time. NeatMeter analyzes the farm’s energy efficiency by monitoring daily electricity use and machine performance by tracking individual equipment health. It also automates equipment maintenance scheduling using predictive maintenance. This allows farmers to efficiently plan maintenance schedules without disrupting daily operations.
Dimaag-AI streamlines Plant Management
Founding Year: 2018
Location: Sunnyvale, USA
Partner for: Asset Health Management, Defect Detection
Industries: Automotive, Electronics, Manufacturing
Dimaag-AI is a US-based startup that provides predictive maintenance for the automotive, electronics, energy, and manufacturing industries. The startup uses ML algorithms to assess past data and build machine failure prediction models using multiple modeling paradigms. Sensors gather data and send them to the cloud, which is later analyzed by data scientists to predict machine failures. Dimaag-AI’s system allows production line operators to efficiently manage their assets’ health.
Discover more Predictive Maintenance Startups
Startups such as the examples highlighted in this report focus on asset monitoring, industrial IoT, energy consumption monitoring as well as wastewater management. While all of these technologies play a major role in advancing maintenance management, they only represent the tip of the iceberg. To explore more predictive maintenance solutions, simply get in touch to let us look into your areas of interest. For a more general overview, you can download one of our free Industry Innovation Reports to save your time and improve strategic decision-making.