FORM 2
THE PATENTS ACT, 1970 (39 of 1970)
PROVISIONAL / COMPLETE SPECIFICATION
(See section 10 and rule 13)

1. TITLE OF THE INVENTION
EQUIPMENT MONITORING METHODS AND SYSTEMS FOR,IMPROVING/OPRIMIZING
EQUIPMENT DESIGN AND PERFORMANCE
2. APPLICANT(S) (a) NAME (b) NATIONALITY (c) ADDRESS
EcoAxis Systems Private Limited
Indian company
3, Shreenivas Classic, 2nd Floor, Baner Road, Pune, Maharashtra, India PIN: 411045
PROVISIONAL
The following specification describes
invention
COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed
4. DESCRIPTION (Description shall start from next page)
5. CLAIMS (not applicable for provisional specification. Claims should start with the preamble
"I/We claim" on separate page)
6. DATE AND SIGNATURE (to be given on the last page of specification)
7. ABSTRACT OF THE INVENTION (to be given along with complete specification on the
separate page)


Equipment Monitoring Methods and Systems for Improving/Optimizing Equipment Design and Performance
APPLICANT:

Name:

EcoAxis Systems Private Limited

Nationality: Indian Company

Address:

3, Shreenivas Classic, 2nd Floor, Baner Road, Pune. Maharashtra, India PIN: 411045

The following specification describes the invention.


Equipment Monitoring Methods and Systems for Improving/Optimizing Equipment Design and Performance
FIELD OF INVENTION
This invention relates generally to solutions (methods and systems) for monitoring equipment in order to improve/optimize equipment feign and performance and, more particularly, to monitoring solutions (methods and systems) for improving/optimizing equipment design and performance over an installed base of several instances of equipment belonging to the same class.
BACKGROUND AND PRIOR ART
Equipment operational expenses form a significant portion of operational expenses of a
manufacturing operation; therefore, optimal performance of equipment has a direct relation
with the cost of goods manufactured.
Equipment performance is not constant and unchanging; many factors such as operating
environment and conditions, quality and specific characteristics of utilities and raw
materials etc. cause the performance or efficiency of equipment to drift from its optimum
or design reference levels.
Monitoring a single process or a single instance of equipment does not allow for discovery
of factors that influence equipment design. Equipment design is done on basis of certain
assumptions related to the conditions in which equipment is operated, the characteristics of
i inputs provided to the equipment, the characteristics of the output produced by the
equipment and the operations and maintenance practices followed in the use of equipment.
The effects of variances in these assumptions on equipment performance are not available
to the equipment manufacturer on a continuous basis using current solutions. The
equipment owner is an expert in the application of equipment to a specific process, but the
equipment design expertise is with the equipment manufacturers, hence it is useful to
provide the equipment performance data to the equipment manufacturer.
The monitoring solution itself has to be efficient in order to ensure that the benefits of
monitoring measure favorably against the cost of data acquisition and analysis.


DESCRIPTION OF RELATED ART
Current equipment management solutions are based on control or process automation, with
the following characteristics:
i
Process automation or automated control systems manage or control the operations of
manufacturing processes and equipment monitoring is an indirect non-primary function of
these solutions. These solutions monitor only those parameters of equipment that are
relevant from the perspective of controlling process or equipment operations.
There are several solutions available that automate the equipment/asset management and
maintenance processes. Only some of these solutions are designed to make use of
equipment health data and within these few even fewer support the feature of directly
acquiring equipment health data. (For example, U.S. Patent 6,871,160 discloses condition
monitoring and maintenance planning for a machine or piece of equipment.). Even in these
cases, jthe usual interfaces provided are to automation/control systems. Most of these
i solutions are designed to be used by asset owners and not by equipment manufacturers.
Similarly, process automation or equipment control systems also provide some monitoring
capabilities but a comprehensive monitoring solution that is aimed at equipment
manufacturers and focused on delivering design improvements through performance
management of installed base is not available currently.
Currently available equipment monitoring solutions are largely in the form of add-ons and
therefore have an unavoidable dependency on the equipment control/process automation
solutions.
i
Most of these solutions are designed to be used by the equipment owners, some of these
solutions are designed to be used by equipment suppliers or service technicians but
conventional solutions (such as U.S. Patent 6,999,903) report data only for error events
deemed to be of importance. A single comprehensive solution that maximizes the value of
continuously monitoring and real time analysis across different stakeholders is not
available.
STATEMENT OF THE INVENTION
Equipment experts (Original Equipment Manufacturers - OEMs) do not currently have
access ;to all of the field data that influences equipment performance, and hence equipment


design is based on information derived from experience, assumption, and models based
simulations etc.
Conventional methods of obtaining data based on which models (of equipment operational
conditions, inputs and energy supplied, operations and maintenance procedures applied,
applications specific configuration of equipment) are based on the following:
1 .R&D'and prototypes built
2.Alpha and beta (field) testing with partially or fully instrumented test equipment
3 .Post product launch surveys and sampling of field data
4.Data culled out from commissioning, trouble-shooting and overhauling reports
In conventional methods.
a. The data thus acquired is limited in scope
b. Data and observations can be corrupted with human bias
c. Sample data is available only for limited time-periods (over which testing
and surveys are conducted)
d. Sample data is usually not collected across all instances of installed
equipment. It may not be practical or even feasible to collect data across all
installed instances e.g. if a tap point is not provided at the time of
manufacturing an equipment, it may not be possible to get a measure of
bearing temperature (without some or the other extent of equipment retrofit,
or in some cases even that might not be an option).
'e. Data acquisition is based on known patterns, existing or assimilated
knowledge
f Specific and explicit efforts are required to maintain consistency and parity
in the data captured by the sampling/testing method (across different installation, operating conditions, user segments etc.). If this is not done, sampling data fed into modeling tools (or other statistical analysis tools such as MATLAB) can produce unreliable results.
Interpretation of sample data is limited to the extent expertise involved at the specific instance of analysis.


h. Expertise for correct analysis and interpretation of sample data may span across several domains and all of the domain experts might or might not
look at the same set of data. In order to make this happen the experts have to
be taken to the data which may need field visits which is not always feasible
or affordable
i. Sample data has to be pro-actively obtained; it is not automatically &
continuously acquired, analyzed or interpreted.
The present teachings enable equipment manufacturers to deliver design improvements that are based on: continuously acquired, accurately measured, automatically analyzed and interpreted data.
In the computer implemented method of these teachings for improving/optimizing equipment design for an installed equipment base, the method includes continuously acquiring, utilizing acquisition systems, (measurements) data for inputs, outputs and energy consumption of each one of a number of pieces of equipment, each one piece of equipment belonging to an installed base of the same class equipment pieces, and analyzing, utilizing one or more processors, the acquired data in order to obtain patterns and relations for the installed base; the patterns and relations comprising factors that affect performance, the patterns and relations being used for equipment design improvement/optimization and installed base management. In one instance, the method also includes recording (storing) the analysis results and providing the analysis results to stakeholders, such as original equipment manufacturers (OEMs) upon request.
i The "measuring" step of the method of the present teachings ensures (by the way of
providing a comprehensive configuration tool with options for different types of
parameters that can be captured) that the data that is relevant for design
verification/validation and performance analysis is accurately and continuously captured.
The "analysis" step comprehensively examines captured data, from the perspective of
identifying factors that affect performance. The "analysis" step is not limited by known
knowledge, or by availability of expert at specific time etc. The step of providing the


analysis results to stakeholders ensures that the analyzed information and its interpretations
are conveyed/presented correctly and automatically to the relevant stakeholders.
j
OBJECTS OF THE INVENTION
The present invention proposes to meet the needs identified in the above description of
related art as well as meeting other needs.
There is a need for a solution that is designed to be used by equipment manufacturers to
improve/optimize design.
There is therefore a need to provide a solution that performs ongoing and continuous
monitoring of equipment performance and operational parameters across a large installed
base to provide equipment performance data to equipment manufacturers in order to:
• Enable continuous improvements in equipment design
• Optimize equipment performance by establishing patterns and correlations that can lead to predictions and preventions of failures and inefficiencies
It is further a need for the monitoring solution to:
• Maximize the intelligence that can be gained out of analysis of the performance
data acquired from an installed base of the equipment without repeating the
i
infrastructure and resources required for data acquisition and analysis
• Communicate the results of analysis of the equipment performance data to various
stakeholders that can benefit from such information to create a feedback loop of
continuous design and performance improvements
There is a need for a solution that is designed to manage an installed base of equipment
across organizational and geographical boundaries.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a schematic block diagram input/output representation of an embodiment of the
system of these teachings;
Figure 2 is a schematic flowchart representation of an embodiment of the method of these
teachings;
Figure G is a schematic flowchart representation of one portion of an embodiment of the
method of these teachings;


Fig. 4 is a schematic block diagram of an embodiment of the system of these teachings;
Fig. 5 lis another schematic block diagram of an embodiment of the system of these
teachings;
Fig. 6 is a schematic block diagram of a component of an embodiment of the system of
these teachings;
Fig. 7 is a schematic block diagram of another component of an embodiment of the system
of these teachings;
Fig. 8 is another schematic block diagram of another component of an embodiment of the
system of these teachings; and
Fig. 9-12 provide details on results of an exemplary embodiment of the method and system
of these teachings.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Computer implemented methods and systems for improving/optimizing equipment design for an installed base of engineering equipment and for managing equipment operation are disclosed herein below. The management of the equipment operation (including improving/optimizing equipment design for an installed base of engineering equipment, hereinaifter referred to as managing equipment operation) can include optimizing equipment performance by establishing patterns and correlations that can lead to predictions and preventions of failures and inefficiencies and improvements in equipment design.
"Continuously" is used herein in the engineering sense. While in the strict sense, continuously is a mathematical abstraction, in the engineering sense, continuous monitoring refers to obtaining a signal whenever the equipment is operating. When applied to digital output, the output of "continuous" monitoring refers to the digitized or sampled output of analog continuous monitoring. In the engineering sense, "continuously" allows for the slight breaks (discontinuities) that occur in real life signal generation. In one embodiment, the method of these teachings includes continuously acquiring, utilizing acquisition systems, data for inputs, outputs and energy consumption of each one of a number of pieces of equipment, each one piece of equipment belonging to an installed


base of a same class a piece of equipment and analyzing, utilizing one or more processors,
the acquired data in order to obtain patterns and relations for the installed base.
In one instance, management of installed base of equipment includes:
• Automated (non-manual) continuous health monitoring of equipment:
o Generating various performance, utilization and efficiency dashboards,
reports, alerts etc. for owners of equipment to optimize equipment
operations, effectively optimizing the OPEX/TCO (operational
expenditure/total cost of ownership) of the equipment. Co-relation of
equipment output production with energy and utilities consumption, and
identification of patterns and deviations in the same results in improved
energy efficiency of the equipment. Accurate equipment performance and
efficiency data allows equipment owners/users to accurately account for
cost of the engineering function performed by the monitored equipment.
o Integrating equipment utilization and performance data with maintenance
schedules to enable condition based preventive maintenance of the
equipment, leads to improved MTBF (mean time between failures) of the
equipment.
• Intelligent Analysis of data from equipment across ownership and geographic
boundaries for continuous design improvements:
o Monitoring solution creates accurate, real-life design validation/verification data.
This scale and scope is difficult/impossible to replicate in labs/test-beds/models. o Analysis of data across an installed base to generate patterns and co-relations of:
• Failures, breakdowns and FTAs (fault tree analysis)
• Relations/dependencies of equipment performance with:
• Input characteristics (e.g. Specifications of raw material and utilities, Specifications of the output produced by the equipment)
• Operating conditions (e.g. Ambient conditions at specific geographical locations)
• Operations and maintenance procedures (e.g. Automated, Manual etc.)


• Application specific configurations and integration (recipes, process specific configurations etc.).
Trends in acquired data that are linked with deviations of performance from
designed/configured levels. Presentation of the results of analysis to different stakeholders in context and medium sensitive manner.
As shown in Fig. 1, in one embodiment of these teachings, equipment is treated as a "black-box" that consumes energy and utilities and to convert certain defined inputs into certain defined outputs and creating some wastes in the process.
In one embodiment, the method and system of these teachings monitors the equipment by measuring the parameters that provide data analysis of equipment's performance and efficiency. The above disclosed framework, shown in Fig. 1, allows equipment experts to configure the parameters that are provides measures of:
• Equipment specific parameters that are useful in deriving the performance and efficiency (10), those that describe the equipment's instance specific operations & maintenance practices, instance or application specific configuration/set-point values

that
are different from those configured by default for the entire class and those that
describe the ambient conditions under which equipment is operating
• The inputs to the equipment (12)
• The energy consumed by the equipment (14)
• The utilities consumed by the equipment (16)
• The output produced by the equipment (18)
• The wastes resulting from the process (20)
Since equipment control systems or automated asset management systems focus on equipment's operations or maintenance, the data available from such systems is in adequate to accurately determine the equipment efficiency and performance.

I
A flowchart description of an embodiment of the method of these teachings is shown in Fig. 2. Referring to Fig. 2, the method includes defining monitoring parameters for a class of equipment in the installed base (Step 25, Fig. 2). The rules/algorithms for analysis for an entire class of equipment are configured (step 30,Fig. 2) and the specific instances of equipment, as they are deployed/installed are also configured (step 35, Fig. 2). The data is continuously acquired and transmitted to backend (remote) systems for analysis (step 40, Fig. 2). In one instance, the data is transmitted over a network such as the Internet. The acquired data is analyzed (continuously, in one instance) (step 45, Fig. 2). From the results of the analysis of the acquired data, notifications/alert of specific conditions can be provided to specific stakeholders (step 55, Fig. 2), such as, but not limited to, OEMs, end users, maintenance & post sales services providers and others. The results of the analysis can be recorded (step 50, Fig. 2) and presented to different stakeholders, such as, but not limited to, OEMs, end users, maintenance and post sales service providers and others, on demand (step 60, Fig. 2). The analysis results when provided to OEMs enable design improve'ments/optimization.
In one embodiment, the method and system of these teachings allows engineering experts to determine which parameters are required to derive equipment performance and efficiency. In order to be efficient in data acquisition, in one instance, the method and system of these teachings provide multiple methods of data acquisition. In one embodiment, the method and system of these teachings support the following different methods for acquiring the parameter values on ongoing basis:
o Configured "set-points" that are common to the entire family of the equipment
o Configured "set-points", those are specific to particular instances of installed equipment (either over-riding the global values set for the entire family or additional values that are relevant to specific instances). This allows for capturing of instance specific data and analysis of the impact on equipment performance of the specific data instances.


o Directly from the equipment's control system ( in situations where the control system is enabled with interfaces to share data)
o Directly from sensors, meters and instruments, this is to support those parameters that are required for measurement of performance and efficiency but are not relevant for equipment operations and controls, and therefore may not be available from the control system.
o Thru manual entry with web or handheld devices (including mobile phones) based interfaces, for parameters that are either not available in an automated manner due to cost of sensors, or due to technical feasibility issues.
o In form of "derived parameters" (Parameters that are derived as a result of calculations performed on some other parameters and/or equipment set-points).

The above disclosed framework further allows equipment experts to configure the rules for interpreting the acquired values individually or in pre-defined or ad-hoc co-related groups on an ongoing basis.
A flowchart description of the details of the analysis step of an embodiment of the method
of these teachings is shown in fig. 3. Referring to fig. 3, the acquired data 65 is filtered in
order to substantially describe invalid samples based on the configured rules (step 70, Fig.
3). From the filter data, the substantially instantaneous values of individual parameters are
evaluated for each instantiation of the equipment (step 75, Fig. 3). The relationship/ratios
between parameter values are evaluated for each instantiation of the equipment (step 80,
Fig. 3). The key performance indicators (KPI) are calculated and evaluated for each
instantiation of the equipment (step 85, Fig. 3). Key performance indicators
i relationship/ratios are evaluating for each instantiation of the equipment (step 90, Fig. 3).
Whether or not the results of steps 75, 80, 85 or 90 represent a failure/breakdown condition
is determined (step 95, Fig. 3). Steps 80, 85, 90 and 95 are repeated over the installed base
of equipment and the results collated (step 97, Fig. 3). At each of steps 75, 80, 85, 90, 95
and 97, the observations are recorded and reported according to the configured
rules/algorithm (step 72. Fig. 3).


This analysis is based on correlating the various parameters of data acquired from the equipment and identification of specific patterns and relations. These patterns and relationships are in the form of comparison/evaluation of measurements over a period of time and on occurrences of specific events.
The each parameter from the acquired data is evaluated against certain configured values and ranges, and based on the results of comparisons; the system triggers further analysis steps or notification of deviations as per configured rules-
The parameters are collated over pre-configured periods of time (e.g. average over a fixed period etc.), these are then compared with similar values of one more other parameters. If deviations from pre-configured limits/ranges are observed, the system records these deviations. The system records all occurrences of such deviations, along with snapshots of parametric data recorded at the time of such occurrences. The system also trends the changes in specific values, ratios between specific parameters or observed deviations over a period of time. The trend itself is examined and matched against pre-configured trends/curves and mismatches/deviations are recorded and acted upon as per configured rules/algorithms.
The equipment experts create and configure rules/algorithms that automate the process of interpreting the data and its analysis as described above, and conversion of the same into meaningful and actionable information. This includes reports, dashboards and other visual data representation tools that give insights into the equipments current status and its productivity, utilization and efficiency. This also includes the logic of identification of
specific pccurrences or of specific conditions or of specific performance indicators (KPIs,
i
e.g. Overall Equipment Effectiveness - OEE, or specific energy consumption of equipment etc.) and trends in the observed values of the same.
All interpretation/analysis rules/algorithms that are configured are executed on automated

ongoing

basis.

The outputs of the interpretation rules and analysis are configured to be presented in various formats (visual/tabular/exported/transmitted etc.) to different defined stakeholders


(equipment manufacturer's design team, maintenance/service teams, owners/operators etc).
In one instance, in one embodiment, the method and system of these teachings defines the following as opportunities for improvement:
o Equipment conditions under which changes in Overall Equipment Effectiveness (OEE) are observed.
o Equipment conditions under which optimal (minimum deviation from design and/or configured levels) consumption of energy is observed.
o Equipment conditions under which optimal consumption of utilities is observed.
o Equipment conditions under which optimal rate of production of output is observed.
o Equipment conditions under which minimum deviation from designed/configured output characteristics are observed.
o Equipment conditions under which equipment failure rate is observed to be at or below the designed or configured rates.
o Equipment conditions that are co-related to the instances to instances of failure, including historical analysis providing frequency distribution of common factors (in terms of parameter values) co-related with instances of equipment failure or breakdown across the entire installed base.
o Identification of characteristics of wastes that indicate opportunities for recycling. A block diagram representation of an embodiment of the system of these teachings is shown ift Fig. 4. Referring to Fig. 4, a number of pieces of equipment, each piece of equipment 105 being an instantiation of a piece of equipment from an installed base, are monitored (parameter values are acquired) by means of a control automation system 110, or sensor/instruments/meters 120, or by entry of the desired parameters 115. The acquired data is provided to a remote or backend system 125 and reports 130 and/or notifications


132 and/or dashboards 134 are obtained and provided to equipment manufacturers (OEMs)
150 anc equipment maintenance and service teams 145 and the equipment users 140.
Another block diagram representation of an embodiment of the system of these teachings is
shown 'in Fig. 5. Referring to Fig. 5, parameters are monitored from each piece of
equipment 105 from a number of pieces of equipment (only one shown), each piece of
equipment 105 being an instantiation of a piece of equipment from an installed base, by
means of a data acquisition component 107. The data acquisition component is interfaced
to a network 117 (exemplary modes of interfacing, not a limitation of these teachings, are
listed) and connected via the network 117 to a remote or backend system (or server) 125.
The remote or backend system 125 is interfaced via a network 117 or other means (such as
SMS gateway 132 to end user operators or OEM representatives (such as OEM service
engineers or OEM management) in order to provide the results of the analysis of the
acquire data.
One embodiment of the remote or backend system (or server) 125 is shown in Fig. 6.
Referring to Fig. 6, the embodiment of the system 125 shown therein includes one or more
processors 160, one or more computer usable media 180 having computer readable code
embodied therein to implement the method of these teachings and a
networlc/communication interface 170, all of which are operatively connected by means of
interconnection component (such as a computer bus) 155.
Embodiments of the data acquisition component 107 are shown in Fig. 7 and Fig. 8.
i Referring to Fig. 7, a data input interface 210 is provided for data acquisition. In the
embodiment shown data output interfaces are not provided since, in one embodiment of the
system of these teachings, the system does not communicate with controi automation logic
unit to provide output, hence net only interfaces with Control/information system for data
acquisition.. The embodiment shown in Fig. 7 can acquire data from the control system
utilizing an industry standard interface, such as, but not limited to, a MODBUS® interface,
or any other interface by means of an interface converter or can acquire data directly from
sensors or instruments with the use of an appropriate signal scanner. A real-time operating
system (RTOS) 220 controls and operates the data acquisition. A real-time clock (RTC)
215 provides timing information/control. A configuration database 230 provided
configuration information which can allow flexible configuring of the measurement


functions. Communication interfaces 240 enable offloading acquired data to a remote analysis server. Functional modules 213 (such as, but not limited to, FTP Client, SMTP Client, 'Web Server) are used for communicating over the communication interfaces 240. Storage, modules 211 provide means for data storage. A more hardware centric description is provided in Fig. 8. Referring to Fig. 8, a processor 250 executes the real-time operating

system

and receives data from a PLC or controller 205 or from sensors 207 connected

through! a scanner 206 by means of an interface 212. The acquired data can be uploaded to a remote analysis server by means of a variety of communication interfaces 240 interfacing to a network 260.
Exemplary embodiments, these teachings not being limited only to those exemplary embodiments, the results of the analysis of the acquired data include the following:

a quick look at plant operations and maintenance scenario including
o Efficiency monitoring: critical for knowing the fuel or power consumption o Runtime monitoring: valuable information on downtime, load patterns o Maintenance monitoring: analysis of frequently occurring alarms and trips that need attention, quality of overhaul done etc.;
a set of reports such as:
o Daily Energy Consumption Report o Weekly Production Report o Monthly Wastages Report;
or results that trace the carbon saving using the methodology of CDM (clean development
mechanism) and help the project optimize and acquire Carbon Emission Reduction, such
as:
• CER Tracking
• Automating CER calculations using "on-line" and "manual entry " data
• Automating data collection process from all projects installed across the country to reduce manpower and time
• Historical data archival at central remote server
• Accurate real time calculation in compliance with "Approved
Methodology"
• CER tracking and forecasting from all installed project activities


• Best M&V practices in relation to documentation and ease of verification
by DOE
!• CER Optimization Module
• Assured CERs through online monitoring
• Optimization of CERs by efficient equipment/plant operations.
A more detailed description of an exemplary embodiment of the method and system of these teachings applied to a CER project is shown in Fig- 9-12. Referring to Fig. 9, in the acquisition stage 270, a data acquisition component 107 acquires input and output parameter data and provides that data to the analysis stage 280. The results of the analysis stage 280 are provided to the reporting stage 290. Sample reports and exemplary applications are also shown. Fig. 10 provides an exemplary list of parameters to be monitored. Fig. 11-12 provided exemplary analysis rules for a CER application. It should be noted that these teachings are capable of producing a variety of other analysis results.
Each computer program within the scope of the claims below may be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language may be a compiled or interpreted programming language. Each computer program may be implemented in a computer program product tangibly embodied in a computer-readable storage device for execution by a computer processor. Method steps of the invention may be performed by a computer processor executing a program tangibly embodied on a computer-readable medium to perform functions of the invention by operating on rnp\A and generating output.
Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CDROM, any other optical medium, punched cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. From a technological standpoint, a signal or carrier wave (such as used for Internet distribution of software) encoded with functional descriptive material is similar to a computer-readable medium encoded with functional descriptive material, in that they both create a functional


interrelationship with a computer. In other words, a computer is able to execute the encoded functions, regardless of whether the format is a disk or a signal. Although these teachings have been described with respect to various embodiments, it should be realized these teachings are also capable of a wide variety of further and other embodiments within the spirit and scope of this invention.
Dated this 29th day of April 2009

Mahua Roy Chowdhury [Authorized Registered Patent Agent for the Applicant]