A to Z of Predictive Maintenance

06 February 2019

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. 

Starting on Wednesday 6th February, we’ll be sharing three letters each week to guide you through the world of Predictive Maintenance.


A - 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_Instagram

B - 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_Instagram-1

C - 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

D is for_Instagram

D - Diagnostics

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

Dr. Simon Kampa

E is for_Instagram

E - 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_Instagram

F - 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
G is for_Instagram

G - 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_Instagram


"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


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