Predictive Maintenance software designed for global industry

Built from the ground-up using sophisticated AI, Senseye PdM is for organizations that need to be confident in the current and future condition of their fleets of machines.

Senseye PdM integrates seamlessly, on any machine and any data source, using data you already collect or with newly installed sensors as part of a complete package. The easy-to-use software automatically generates machine and maintainer behavior models to help direct your attention and expertise to where it’s needed most.

There is no need for extensive condition monitoring or data science expertise to start seeing results from the cloud-based software. For every company that can benefit from reduced machine downtime, increased sustainability and reduced operations and maintenance costs, there’s a Senseye PdM option to suit you.

“For organizations already on the Industry 4.0 journey, deployment is near-instant and actionable results are delivered in a maximum of 14 days, enabling our customers to experience a leading ROI of less than 3 months.” Robert Russell, Senseye CTO

What is Predictive Maintenance?

Machinery in production environments needs to be maintained regularly to ensure optimal performance. Predictive maintenance (PdM) uses artificial intelligence (AI) and machine learning (ML) to automatically interpret industrial machines' condition now and in the future, with the ultimate goal of saving operational and maintenance expenses.

It is much more than applying condition monitoring in your maintenance strategy as you might be doing with Condition Based Maintenance (CBM). It’s about predicting upcoming machinery issues so that failures can be avoided, and the correct maintenance actions can be performed when they’re needed. Additionally PdM can enable prognostics – the ability to predict Remaining Useful Life.

The benefits of automating the collection and analysis of machine-health data as well as simplifying the reporting of resulting actions means maintainers can focus their time, monitor more machines and be more effective.

Senseye PdM enables you to

Reduce downtime

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Improve sustainability

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Ensure precision maintenance

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Support mobile workers

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Reduce operating costs

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Want to see it in action?

Request a live demo of Senseye PdM and learn how you can benefit

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See what you could save

Find out how much money your organization could save with our RoI calculator

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Powered by knowledge and expertise

Senseye Predictive Maintenance is built from decades of experience in condition monitoring from industries like aerospace & defense. We’ve infused this knowledge and experience into the AI that powers Senseye PdM. Not only does it know what to look for, but it continuously learns from your machine's and maintenance team's unique behaviour. With access to our dedicated team of PdM professionals and resources, we’re here to guide you on your unique predictive maintenance journey.

Unparalleled scalability - every machine, any sensor

Manually developing custom models for each type of machine is time-consuming. Senseye PdM uses cutting-edge machine learning approaches to do this automatically without user intervention, so you can rapidly apply predictive maintenance to thousands of devices across multiple sites at scale. It can use data already collected by your control systems or through retrofitted sensors to ensure that you’re maximising your existing investments.

Maintainer focused

Senseye PdM can be used directly by maintainers working remotely or on the shop floor. It’s the only predictive maintenance application to build models of the machine and user behaviour, ensuring users get the insights they want, when they want it, on desktop or mobile, and in a modern, easy to use interface. This approach allows our AI to work collaboratively with your maintenance team, enhancing knowledge and expertise.

Integrated with industry leaders

Senseye has an unmatched global partner network. We support everything from sensor specification and commissioning to ongoing service and support with leading industrial integrators and manufacturers. Senseye PdM also has a fully accessible API, allowing your team to integrate seamlessly with your existing technology stack, customize your requirements, and leverage your in-house skills and IP.

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How does Senseye PdM work?

Senseye’s award-winning predictive maintenance software, powered by proprietary technology, is a cloud-based platform capable of processing vast volumes of data. Currently more than 1 million machine-related data points per minute across thousands of different machine types globally.

Designed to integrate seamlessly and provide maximum value, Senseye PdM achieves 85% improvement in downtime forecasting accuracy through machine health diagnostics and prognostics.
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How can we help?

Senseye is trusted by Fortune 500 customers worldwide, across a wide variety of industries, to save millions of dollars in unplanned downtime and maintenance efficiencies every week. No matter where you are on your maintenance journey, the benefits of scalable predictive maintenance are straightforward and can be achieved quickly from any starting point. Here’s how we can help you.

Senseye PdM Starter

No sensors, no data, no problem.

All the hardware you need to start collecting data from your machinery in as little as 14 days.

Includes:

Pre-configured hardware and sensor technology 

Senseye PdM, AI driven predictive maintenance analytics software

Plus all the additional benefits of Senseye PdM Complete

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Senseye PdM Complete

So effective, it’s guaranteed.

Award-winning software and managed guidance on the journey to achieve full PdM ROI within 12 months.

Includes:

Senseye ROI Lock® guarantee - success or your money back

Expert setup and guidance by the Senseye Customer Success Management team

Senseye PdM, AI driven predictive maintenance analytics software

Global partners access to fill data and deployment gaps

Custom dashboards, data visualisations and reporting

Easy to use multi-device, multi-language user interface

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Senseye PdM Enterprise

Power your own team’s journey.

Senseye PdM with additional options to integrate and manage your own digital transformation journey at scale.

Includes:

Senseye PdM, AI driven predictive maintenance analytics software

Advanced algorithm configuration and admin tools

Easy multi-site administration and configuration

Single-sign-on integration

Open and extendable integrations and APIs

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What our customers say

  • Alcoa-logo-6-june
    We are pleased to have partnered with Senseye on this corporate initiative to enable Predictive Maintenance and improve operational efficiencies. The results and ROI were executed in a prompt manner, and our users are thrilled with the ease of use of the Senseye PdM product.
    Bruno Longchamps Global Aluminum Manufacturing Intelligence Manager
  • Smurfitt-kappa-logo-june-21
    Over the last five years we have been on a journey towards predictive maintenance. In 2020 we stepped positively into an online predictive maintenance arena with Senseye. This approach will enable further improvement across our engineering functions as we roll out the approach across other sites driving equipment reliability.
    Steve Parr Engineering Director
  • Nissan_logo_05_21
    Senseye is supporting our Predictive Maintenance program across multiple production facilities and has helped us lower overall downtime and increase OEE.
    Damian Wheler UK Engineering Director
  • Schneider_Electric_15_21
    Senseye together with Schneider Electric, enable the co-creation of solutions and enriching learning and speed through collective intelligence. Together this digital ecosystem creates, collaborates, and scales business growth.
    Cyril Perducat EVP IoT & Digital Offers

The history of Predictive Maintenance

Predictive maintenance is not new. With its roots in observations made by scientist CH Waddington concerning the maintenance of Royal Air Force bombers during the Second World War, PdM has, in fact, been around for over 75 years.

Observations that a plane’s rate of failure or repair tended to be highest immediately after an inspection or maintenance session - a phenomenon later dubbed the “Waddington Effect” - led to the first development in condition-based maintenance. Maintenance processes were adjusted to correspond with the equipment’s physical condition and the frequency of its use, and the resulting data compiled and analyzed to form the basis of adjusted inspection cycles, thereby heralding the beginnings of PdM.

Predictive maintenance today is an even more proactive approach. Analyzing huge volumes of available machinery data to get a better understanding of the ongoing health of machines and effectively pre-empting failure. By predicting when machinery will break down, companies can remove surprise failures and reduce downtime, scheduled maintenance, and the routine replacement of parts that may, in fact, be perfectly healthy.

More about Maintenance Methodologies

A-Z of Predictive Maintenance

Whether you are new or already familiar with Predictive Maintenance (PdM), the Senseye team have put together a handy A to Z guide of commonly used words and phrases associated with the maintenance practice.

A is for Anomalousness

"Anomalies are features in the data that are unexpected or unexplained. Anomalousness is a measure of the density of anomalies, or put another way, a measure of how abnormal the data appears."

Dr. James Loach - Chief Scientist

B is for Bathtub Curve

"This is a classic representation of the probability of machine failure over the lifecycle of an asset. At the beginning, failure is more likely due to manufacturing and installation errors (early failures). For the majority of an asset's life, failure rates become constant (random failures). Towards the end, the probability of failure begins to increase as components succumb to fatigue (wear-out failures)."

Chris Esprey - Condition Monitoring Specialist

C is for Condition Monitoring

"Condition Monitoring (CM) is a discipline that makes use of sensor measurements with processing to determine the condition of industrial machines. CM enables many business benefits and forms the basis of maturing maintenance practices from reactive to preventative to predictive."

Rob Russell - CTO 

D is for Diagnostics

"The examination of symptoms and syndromes to determine the nature of faults or failures."

Dr. Simon Kampa - CEO

E is for Expert Knowledge

"Expert knowledge, or context, is information used to interpret data. In our domain, it describes the characteristics of assets, how they operate and how they fail. It is also used to select condition indicators."

Dr. Samuel Park - Data Scientist

F is for Failure Modes

"Machines don't fail in a single consistent way. There are many moving parts that degrade over time. Failure modes are a way of describing each type of failure that a machine can experience, but importantly using a consistent term or code for each failure mode. Good practice in capturing failure modes as part of maintenance work recording is an investment in the value of your contrition monitoring data, as it provides context to the data and enhances the type of analysis that can be performed."

Rob Russell - CTO

G is for Ground Truth

"Ground truth" is the name given to any domain-specific benchmark that can be used to quantify and judge the predictive capabilities of a machine learning model that has been applied to that domain. When used appropriately, 'Ground truth' can be used as a model selection tool."

Dr. Samuel Park - Data Scientist

H is for HUMS

"Originating from aerospace, Health and Usage Monitoring Systems (HUMS) are designed to automatically monitor the health of mechanical components, as well as the usage of an airframe and its dynamic components. HUMS have been shown to enhance safety, decrease maintenance burden, increase availability and readiness and reduce operating and support costs. The HUMS concept is now making its way into other industries, though under different names."

Rob Russell - CTO 

I is for Industry 4.0

"It's broadly agreed that there are four industrial revolutions: The first representing a change from agricultural to urban environments, the second representing mass production, the third for digital automation and the fourth - connectedness. Industry 4.0 represents the infusion of Information Technology into every layer of machine, resulting in Operational Technology and producing massive amounts of data that enable intelligence and optimization never before possible."

Alex Hill - Senseye USA CEO

J is for Just in Time

"Just in Time (JIT) is a fundamental manufacturing methodology originating in the Japanese manufacturing sector in the 1960's. It focuses on shorting manufacturing cycles and reducing the need for stock holding across the entire supply chain.  The benefits affect every part of the business: the cost of inventory storage, more efficient production and product quality improvements."
 
Rob Russell - CTO

K is for Kalman Filter

"An approach where you use knowledge about the system states and educated guesses about uncertainties to help you make reasonable inferences about the future evolution of the system. When it all comes together it is almost magical."  

Dr. Lee Middleton - Data Scientist

L is for Lubrication

"Lubrication is the process by which the friction/wear between two surfaces is reduced. This is commonly achieved in machinery via an application of a lubricant such as oil or grease. Changes in the particulate and moisture content of the lubricant can be used to infer wear of machine components and prompt maintenance action."

Chris Esprey - Condition Monitoring Specialist

M is for Machine Learning

"Algorithms are instructions for turning inputs into outputs. Humans can write them, but when this is hard or slow, we can delegate the task to a computer. The algorithms that computers use to write algorithms—to infer them from samples of inputs and outputs—are called machine learning algorithms. And they are what machine learning is all about." 

Dr. James Loach - Chief Scientist

N is for Notifications

"Notifications are a key way to direct user attention to the right thing. It’s a fine line between too much information (‘I have too much work to do!’) and too little (‘This thing is broken!’) and one that can only be understood by keeping close relationships with regular users of the product."
 
Adam Poole - Product Design Lead

O is for Operational Equipment Effectiveness (OEE)

"Operational Equipment Effectiveness (OEE) is a key measure of manufacturing productivity. It shows the productive percentage of manufacturing time by combining 3 elements of machine performance: availability (A), performance (P), and output quality (Q). Achieving an OEE score of 100% means that only good parts are manufactured, as fast as possible, and with no down time."

Lily Hristova - Product Associate

P is for Prognostics

"Prognostics is the engineering discipline of predicting machine failure. There are two main approaches, model driven uses physical understanding of the machine to predict failures, whereas data driven uses historic data and machine learning. We blend the two, using our condition monitoring expertise to bolster data driven techniques."

Dr. Daniel Reid - Backend Lead

Q is for Quantisation Error

"Quantisation error is the process by which noise is added to a signal by the sample values being constrained to a discrete set of numbers. Quantisation error can be introduced by the use of sensors which have a low resolution relative to the features of interest in the data and can lead to reduced sensitivity in condition monitoring applications."

Graham Bruce - Condition Monitoring Specialist

R is for Root Cause

"The root cause is the fundamental reason for the occurrence of a problem. The term denotes the earliest, most basic cause of the fault. Fixing a problem at its root therefore removes the cause at its deepest layer, eradicating it completely."
 
Peggy Cooper-Berger - Marketing Associate

S is for Scalable

"Scalability means the capacity to change the scale of any given project - from one machine to tens of thousands. The amount of data that can be captured from modern machines is enormous and increasing all the time. In order to harness it, we design our systems so that we can monitor the 100'000th sensor just as accurately and as quickly as the first."

Oliver Skånberg-Tippen - Machine Learning Engineer

T is for Trend Detection

"Unexpected trends in data from mechanical equipment indicate significant and persistent change in machine state. This is often a sign of degradation in the run up to machine failure, thus detecting them at the right time is an important capability."

Dr. Henry Truong - Data Scientist

U id for Unplanned

"From your commute ruined by a broken-down train, to your halted production line due to machine failure, unplanned downtime can be inconvenient and costly. One of condition monitoring's aims is to find early indicators of failure so intervention can avoid catastrophic events."

Dr. Chris Smith - Condition Monitoring Specialist

V is for Vibration

"Vibration Analysis is a keystone technique in the condition monitoring of rotating machinery as small changes in the structure of mechanical components (such as bearings and gears) have a large and predictable effect on the vibration response of the component – this allows for the early identification of degradation and failure of said component."

Chris Esprey - Condition Monitoring Specialist

W is for Web App

"Senseye's web app allows maintenance teams to quickly and easily interpret vast quantities of data, keeping their focus on performing critical maintenance activities. We bring the most important information to the forefront, provide the tools to analyse and understand the underlying data, all in support of real world decision making."

Matt Wilson - Frontend Lead

X is for XOr

"The XOr, or 'Exclusive Or', is a classic problem in machine learning. It uses a neural network to predict the outputs of XOr logic gates given two binary inputs. It should return a false value if the two inputs are equal and true otherwise. Complex models like this always need to be changed and adapted to solve problems for real machines." 

Henry Truong - Data Scientist

Y is for Yaw

"Yaw rotation is the movement of a rigid body about its vertical axis. As a key axis of movement for machines ranging from industrial robots to wind turbines, yaw is frequently a focal point for condition monitoring applications."

Graham Bruce - Condition Monitoring Specialist

Z is for Zero

"Zero was a bot from the hood who instead of moving just stood his measures were flustered
so the bot was adjusted giving Zero no downtime for good."

Dr. Lee Middleton - Data Scientist